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  • Davidsen S., Padmavathamma M., 2014. A fuzzy closeness centrality using andness-direction to control degree of closeness. 1st International Conference on Networks and Soft Computing, ICNSC 2014 - Proceedings, , pp.203-208.DOI: 10.1109/CNSC.2014.6906711
  • Jianwei W., Lili R., Tianzhu G., 2008. A new measure of node importance in complex networks with tunable parameters. 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, .DOI: 10.1109/WiCom.2008.1170
  • Ilyas M., Radha H., 2010. A KLT-inspired node centrality for identifying influential neighborhoods in graphs. 2010 44th Annual Conference on Information Sciences and Systems, CISS 2010, .DOI: 10.1109/CISS.2010.5464971
  • Chua H., Bhowmick S., Tucker-Kellogg L., Zhao Q., Dewey C., Yu H., 2011. PANI: A novel algorithm for fast discovery of Putative TArget Nodes in signaling networks. 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011, , pp.284-288.DOI: 10.1145/2147805.2147836
  • Niu J., Fan J., Wang L., Stojinenovic M., 2014. K-hop centrality metric for identifying influential spreaders in dynamic large-scale social networks. 2014 IEEE Global Communications Conference, GLOBECOM 2014, , pp.2954-2959.DOI: 10.1109/GLOCOM.2014.7037257
  • Stai E., Sotiropoulos K., Karyotis V., Papavassiliou S., 2016. Hyperbolic Traffic Load Centrality for large-scale complex communications networks. 2016 23rd International Conference on Telecommunications, ICT 2016, .DOI: 10.1109/ICT.2016.7500371
  • Chakraborty T., Narayanam R., 2016. Cross-layer betweenness centrality in multiplex networks with applications. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, , pp.397-408.DOI: 10.1109/ICDE.2016.7498257
  • Garzon C., Pavas A., 2017. Laplacian eigenvector centrality as tool for assessing causality in power quality. 2017 IEEE Manchester PowerTech, Powertech 2017, .DOI: 10.1109/PTC.2017.7981261
  • Alshahrani M., Fuxi Z., Sameh A., Mekouar S., Huang S., 2018. Top-K influential users selection based on combined Katz centrality and propagation probability. 2018 3rd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2018, , pp.52-56.DOI: 10.1109/ICCCBDA.2018.8386486
  • Ghalmane Z., Hassouni M.E., Cherifi H., 2018. Betweenness Centrality for Networks with Non-Overlapping Community Structure. 2018 IEEE Workshop on Complexity in Engineering, COMPENG 2018, .DOI: 10.1109/CompEng.2018.8536229
  • Khan J.A., Westphal C., Ghamri-Doudane Y., 2018. A Popularity-aware Centrality Metric for Content Placement in Information Centric Networks. 2018 International Conference on Computing, Networking and Communications, ICNC 2018, , pp.554-560.DOI: 10.1109/ICCNC.2018.8390396
  • Li B., Gao Z., Shan X., Zhou W., Ferrara E., 2019. Sorec: A social-relation based centrality measure in mobile social networks. 2019 26th International Conference on Telecommunications, ICT 2019, , pp.485-489.DOI: 10.1109/ICT.2019.8798844
  • Espejo R., Lumbreras S., Ramos A., Huang T., Bompard E., 2019. An extended metric for the analysis of power-network vulnerability: The line electrical centrality. 2019 IEEE Milan PowerTech, PowerTech 2019, .DOI: 10.1109/PTC.2019.8810514
  • Das K., Samanta S., De K., Pal M., 2020. Complete neighbourhood centrality and its application. 4th International Conference on Computational Intelligence and Networks, CINE 2020, .DOI: 10.1109/CINE48825.2020.234386
  • Masaaki Miyashita and Norihiko Shinomiya. 2015, Modified Betweenness Centrality to Identify Relay Nodes in Data Networks. ACHI 2015 : The Eighth International Conference on Advances in Computer-Human Interactions.
  • Hamilton K., Mintz T., Date P., Schuman C.D., 2020. Spike-based graph centrality measures. ACM International Conference Proceeding Series, .DOI: 10.1145/3407197.3407199
  • Lempel R., Moran S., 2002. SALSA: The stochastic approach for link-structure analysis. ACM Transactions on Information Systems, 19(2), pp.131-160.DOI: 10.1145/382979.383041
  • Lee K.H., Kim M.H., 2020. Linearization of dependency and sampling for participation-based betweenness centrality in very large b-hypergraphs. ACM Transactions on Knowledge Discovery from Data, 14(3).DOI: 10.1145/3375399
  • Riondato M., Upfal E., 2018. ABRA: Approximating betweenness centrality in static and dynamic graphs with rademacher averages. ACM Transactions on Knowledge Discovery from Data, 12(5).DOI: 10.1145/3208351
  • P. Marjai, A. Kiss., 2020, Efficiency centrality in time-varying graphs. Acta Universitatis Sapientiae, Informatica, 12, 1, 5−21.DOI: 10.2478/ausi-2020-0001
  • Glattfelder J., 2019. THE BOW-TIE CENTRALITY: A NOVEL MEASURE for DIRECTED and WEIGHTED NETWORKS with AN INTRINSIC NODE PROPERTY. Advances in Complex Systems, .DOI: 10.1142/S0219525919500188
  • Ide, K., Namatame, A., Ponnambalam, L., Xiuju, F. and Goh, R.S.M., 2014. A new centrality measure for probabilistic diffusion in network. Advances in Computer Science: An International Journal, 3(5), pp.115-121.
  • Das A., Biswas A., 2021. Rumor Source Identification on Social Networks: A Combined Network Centrality Approach. Advances in Intelligent Systems and Computing, 1299 AISC, pp.269-280.DOI: 10.1007/978-981-33-4299-6_22
  • Cauteruccio, F., Terracina, G., Ursino, D. and Virgili, L., 2019. Redefining Betweenness Centrality in a Multiple IoT Scenario. In AI&IoT@ AI* IA (pp. 16-27).
  • Ivanov S., Gorlushkina N., Ivanova L., 2018. Multi-parametric centrality method for graph network models. AIP Conference Proceedings, 1952.DOI: 10.1063/1.5032005
  • Nathan E., Zakrzewska A., Riedy J., Bader D., 2017. Local community detection in dynamic graphs using personalized centrality. Algorithms, 10(3).DOI: 10.3390/a10030102
  • Bonacich, P., 1987. Power and centrality: A family of measures. American journal of sociology, 92(5), pp.1170-1182.DOI: 10.1086/228631
  • Burt R., 2004. Structural holes and good ideas. American Journal of Sociology, 110(2), pp.349-399.DOI: 10.1086/421787
  • Tsiotas D., Polyzos S., 2015. Introducing a new centrality measure from the transportation network analysis in Greece. Annals of Operations Research, 227(1), pp.93-117.DOI: 10.1007/s10479-013-1434-0
  • Wang D., Zou X., 2018. A new centrality measure of nodes in multilayer networks under the framework of tensor computation. Applied Mathematical Modelling, 54, pp.46-63.DOI: 10.1016/j.apm.2017.07.012
  • Agryzkov T., Oliver J., Tortosa L., Vicent J., 2012. An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector. Applied Mathematics and Computation, 219(4), pp.2186-2193.DOI: 10.1016/j.amc.2012.08.064
  • Agryzkov T., Tortosa L., Vicent J., 2016. New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks. Applied Mathematics and Computation, 291, pp.14-29.DOI: 10.1016/j.amc.2016.06.036
  • Cerdeira, J.O. and Silva, P.C., 2021. A centrality notion for graphs based on Tukey depth. Applied Mathematics and Computation, 409, p.126409.DOI: 10.1016/j.amc.2021.126409
  • Agryzkov T., Oliver J., Tortosa L., Vicent J., 2014. A new betweenness centrality measure based on an algorithm for ranking the nodes of a network. Applied Mathematics and Computation, 244, pp.467-478.DOI: 10.1016/j.amc.2014.07.026
  • Wang J., Li C., Xia C., 2018. Improved centrality indicators to characterize the nodal spreading capability in complex networks. Applied Mathematics and Computation, 334, pp.388-400.DOI: 10.1016/j.amc.2018.04.028
  • Liu W.C., Huang L.C., Liu C.W.J., Jordán F., 2020. A simple approach for quantifying node centrality in signed and directed social networks. Applied Network Science, 5(1).DOI: 10.1007/s41109-020-00288-w
  • Meghanathan, N., 2021. Neighborhood-based bridge node centrality tuple for complex network analysis. Applied Network Science, 6(1), pp.1-36.DOI: 10.1007/s41109-021-00388-1
  • Béres F., Pálovics R., Oláh A., Benczúr A.A., 2018. Temporal walk based centrality metric for graph streams. Applied Network Science, 3(1).DOI: 10.1007/s41109-018-0080-5
  • Vukičević, D., Škrekovski, R. and Tepeh, A., 2016. Relative edge betweenness centrality. Ars Mathematica Contemporanea, 12(2), pp.261-270.DOI: 10.26493/1855-3974.863.169
  • Kang C., Kraus S., Molinaro C., Spezzano F., Subrahmanian V., 2016. Diffusion centrality: A paradigm to maximize spread in social networks. Artificial Intelligence, 239, pp.70-96.DOI: 10.1016/j.artint.2016.06.008
  • Skibski O., Rahwan T., Michalak T., Yokoo M., 2019. Attachment centrality: Measure for connectivity in networks. Artificial Intelligence, 274, pp.151-179.DOI: 10.1016/j.artint.2019.03.002
  • Gurfinkel, A.J. and Rikvold, P.A., 2020. A Current-Flow Centrality With Adjustable Reach. arXiv preprint arXiv:2005.14356.
  • Magelinski, T., Bartulovic, M. and Carley, K.M., 2020. Modularity-Impact: a Signed Group Centrality Measure for Complex Networks. arXiv preprint arXiv:2003.00056.
  • Boito, P. and Grena, R., 2021. Quantum hub and authority centrality measures for directed networks based on continuous-time quantum walks. arXiv preprint arXiv:2104.09637.
  • Huang, S., Cui, H. and Ding, Y., 2014. Evaluation of node importance in complex networks. arXiv preprint arXiv:1402.5743.
  • Amshi, A.T. and Shu, J., 2020. Complex Network Influence Evaluation based on extension of Grueblers Equation. arXiv preprint arXiv:2012.13617.DOI: 10.13140/RG.2.2.14025.36960
  • Mazalov V.V., Khitraya V.A., 2021. A Modified Myerson Value for Determining the Centrality of Graph Vertices. Automation and Remote Control, 82(1), pp.145-159.DOI: 10.1134/S0005117921010100
  • Naderi Yeganeh P., Naderi Yeganeh P., Richardson C., Saule E., Loraine A., Taghi Mostafavi M., 2020. Revisiting the use of graph centrality models in biological pathway analysis. BioData Mining, 13(1).DOI: 10.1186/s13040-020-00214-x
  • Prifti E., Zucker J.D., Clément K., Henegar C., 2010. Interactional and functional centrality in transcriptional co-expression networks. Bioinformatics, 26(24), pp.3083-3089.DOI: 10.1093/bioinformatics/btq591
  • Pržulj N., Wigle D., Jurisica I., 2004. Functional topology in a network of protein interactions. Bioinformatics, 20(3), pp.340-348.DOI: 10.1093/bioinformatics/btg415
  • Parvandeh S., McKinney B.A., 2019. Epistasisrank and Epistasiskatz: Interaction network centrality methods that integrate prior knowledge networks. Bioinformatics, 35(13), pp.2329-2331.DOI: 10.1093/bioinformatics/bty965
  • Cickovski T., Peake E., Aguiar-Pulido V., Narasimhan G., 2017. ATria: A novel centrality algorithm applied to biological networks. BMC Bioinformatics, 18.DOI: 10.1186/s12859-017-1659-z
  • Potapov A., Goemann B., Wingender E., 2008. The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks. BMC Bioinformatics, 9.DOI: 10.1186/1471-2105-9-227
  • Sun M.W., Moretti S., Paskov K.M., Stockham N.T., Varma M., Chrisman B.S., Washington P.Y., Jung J.Y., Wall D.P., 2020. Game theoretic centrality: A novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value. BMC Bioinformatics, 21(1).DOI: 10.1186/s12859-020-03693-1
  • del Rio G., Koschützki D., Coello G., 2009. How to identify essential genes from molecular networks?. BMC Systems Biology, 3, pp.102.DOI: 10.1186/1752-0509-3-102
  • Li M., Zhang H., Wang J., Pan Y., 2012. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Systems Biology, 6.DOI: 10.1186/1752-0509-6-15
  • Stelzl U., Worm U., Lalowski M., Haenig C., Brembeck F.H., Goehler H., Stroedicke M., Zenkner M., Schoenherr A., Koeppen S., Timm J., Mintzlaff S., Abraham C., Bock N., Kietzmann S., Goedde A., Toksöz E., Droege A., Krobitsch S., Korn B., Birchmeier W., Lehrach H., Wanker E.E., 2005. A human protein-protein interaction network: A resource for annotating the proteome. Cell, 122(6), pp.957-968.DOI: 10.1016/j.cell.2005.08.029
  • Pedroche F., Romance M., Criado R., 2016. A biplex approach to PageRank centrality: From classic to multiplex networks. Chaos, 26(6).DOI: 10.1063/1.4952955
  • Berahmand K., Bouyer A., Samadi N., 2018. A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos, Solitons and Fractals, 110, pp.41-54.DOI: 10.1016/j.chaos.2018.03.014
  • Ibnoulouafi A., El Haziti M., 2018. Density centrality: identifying influential nodes based on area density formula. Chaos, Solitons and Fractals, 114, pp.69-80.DOI: 10.1016/j.chaos.2018.06.022
  • Du, Y., Gao, C., Chen, X., Hu, Y., Sadiq, R. and Deng, Y., 2015. A new closeness centrality measure via effective distance in complex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(3), p.033112.DOI: 10.1063/1.4916215
  • XU, G.-Q., MENG, L., TU, D.-Q. & YANG, P.-L. 2021. LCH: a local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks. Chinese Physics B.DOI: 10.1088/1674-1056/abea86
  • Euler, L., 1741. Solutio problematis ad geometriam situs pertinentis. Commentarii academiae scientiarum Petropolitanae, pp.128-140.
  • Caporossi, G., Paiva, M., Vukičevic, D. and Segatto, M., 2012. Centrality and betweenness: vertex and edge decomposition of the Wiener index. MATCH-Communications in Mathematical and Computer Chemistry, 68(1), p.293.
  • Wang S., Du Y., Deng Y., 2017. A new measure of identifying influential nodes: Efficiency centrality. Communications in Nonlinear Science and Numerical Simulation, 47, pp.151-163.DOI: 10.1016/j.cnsns.2016.11.008
  • Andrade R., Rêgo L., 2019. p-means centrality. Communications in Nonlinear Science and Numerical Simulation, 68, pp.41-55.DOI: 10.1016/j.cnsns.2018.08.002
  • Ullah A., Wang B., Sheng J., Long J., Khan N., 2021. Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks. Complexity, 2021.DOI: 10.1155/2021/8403738
  • Ma X., Ma Y., 2019. The Local Triangle Structure Centrality Method to Rank Nodes in Networks. Complexity, 2019.DOI: 10.1155/2019/9057194
  • Herzog S.M., Hills T.T., 2019. Mediation Centrality in Adversarial Policy Networks. Complexity, 2019.DOI: 10.1155/2019/1918504
  • Akgün M.K., Tural M.K., 2020. k-step betweenness centrality. Computational and Mathematical Organization Theory, 26(1), pp.55-87.DOI: 10.1007/s10588-019-09301-9
  • Li M., Wang J., Chen X., Wang H., Pan Y., 2011. A local average connectivity-based method for identifying essential proteins from the network level. Computational Biology and Chemistry, 35(3), pp.143-150.DOI: 10.1016/j.compbiolchem.2011.04.002
  • Ghaffar F., Hurley N., 2020. Structural hole centrality: evaluating social capital through strategic network formation. Computational Social Networks, 7(1).DOI: 10.1186/s40649-020-00079-4
  • Kermarrec A.M., Le Merrer E., Sericola B., Trédan G., 2011. Second order centrality: Distributed assessment of nodes criticity in complex networks. Computer Communications, 34(5), pp.619-628.DOI: 10.1016/j.comcom.2010.06.007
  • Khadangi E., Bagheri A., 2017. Presenting novel application-based centrality measures for finding important users based on their activities and social behavior. Computers in Human Behavior, 73, pp.64-79.DOI: 10.1016/j.chb.2017.03.014
  • Agha Mohammad Ali Kermani M., Badiee A., Aliahmadi A., Ghazanfari M., Kalantari H., 2016. Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm. Computers in Human Behavior, 56, pp.295-305.DOI: 10.1016/j.chb.2015.11.008
  • Liu G., Yao X., Luo Z., Kang S., Long W., Fan Q., Gao P., 2019. Agglomeration centrality to examine spatial scaling law in cities. Computers, Environment and Urban Systems, 77.DOI: 10.1016/j.compenvurbsys.2019.101357
  • Zhang B., Zhang L., Mu C., Zhao Q., Song Q., Hong X., 2019. A most influential node group discovery method for influence maximization in social networks: A trust-based perspective. Data and Knowledge Engineering, 121, pp.71-87.DOI: 10.1016/j.datak.2019.05.001
  • Chang Y.C., Lai K.T., Chou S.C.T., Chiang W.C., Lin Y.C., 2021. Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk. Data Technologies and Applications, 55(1), pp.1-18.DOI: 10.1108/DTA-05-2020-0103
  • Kirkland S., 2010. Algebraic connectivity for vertex-deleted subgraphs, and a notion of vertex centrality. Discrete Mathematics, 310(4), pp.911-921.DOI: 10.1016/j.disc.2009.10.011
  • Gouveia C., Móréh Á., Jordán F., 2021. Combining centrality indices: Maximizing the predictability of keystone species in food webs. Ecological Indicators, 126.DOI: 10.1016/j.ecolind.2021.107617
  • Kong, R., Han, C., Guo, T. and Pei, W., 2013. An Energy-Based Centrality for Electrical Networks. Energy and Power Engineering, 5(04), p.597.DOI: 10.4236/epe.2013.54B115
  • Williams, J., 2019. Identifying sensitive components in infrastructure networks via critical flows. engrXiv.
  • Qiao T., Shan W., Yu G., Liu C., 2018. A novel entropy-based centrality approach for identifying vital nodes in weighted networks. Entropy, 20(4).DOI: 10.3390/e20040261
  • Qiao T., Shan W., Zhou C., 2017. How to identify the most powerful node in complex networks? A novel entropy centrality approach. Entropy, 19(11).DOI: 10.3390/e19110614
  • Stella M., De Domenico M., 2018. Distance entropy cartography characterises centrality in complex networks. Entropy, 20(4).DOI: 10.3390/e20040268
  • Agryzkov T., Tortosa L., Vicent J.F., Wilson R., 2019. A centrality measure for urban networks based on the eigenvector centrality concept. Environment and Planning B: Urban Analytics and City Science, 46(4), pp.668-689.DOI: 10.1177/2399808317724444
  • Hellervik A., Nilsson L., Andersson C., 2019. Preferential centrality – A new measure unifying urban activity, attraction and accessibility. Environment and Planning B: Urban Analytics and City Science, 46(7), pp.1331-1346.DOI: 10.1177/2399808318812888
  • Zhang Q., Karsai M., Vespignani A., 2018. Link transmission centrality in large-scale social networks. EPJ Data Science, 7(1).DOI: 10.1140/epjds/s13688-018-0162-8
  • Piraveenan M., Prokopenko M., Zomaya A., 2008. Local assortativeness in scale-free networks. EPL, 84(2).DOI: 10.1209/0295-5075/84/28002
  • Li C., Li Q., Van Mieghem P., Stanley H.E., Wang H., 2015. Correlation between centrality metrics and their application to the opinion model. European Physical Journal B, 88(3), pp.1-13.DOI: 10.1140/epjb/e2015-50671-y
  • Šikić M., Lančić A., Antulov-Fantulin N., Štefančić H., 2013. Epidemic centrality - Is there an underestimated epidemic impact of network peripheral nodes?. European Physical Journal B, 86(10).DOI: 10.1140/epjb/e2013-31025-5
  • Pal S., Kundu S., Murthy C., 2014. Centrality measures, upper bound, and influence maximization in large scale directed social networks. Fundamenta Informaticae, 130(3), pp.317-342.DOI: 10.3233/FI-2014-994
  • Tew K.L., Li X.L., Tan S.H., 2007. Functional centrality: detecting lethality of proteins in protein interaction networks.. Genome informatics. International Conference on Genome Informatics, 19, pp.166-177.DOI: 10.1142/9781860949852_0015
  • Punithavelan, N. and Jaganathan, B., 2017. New web page rank method using HITS Centrality. Global Journal of Pure and Applied Mathematics, 13(10), pp.7229-7235.
  • Pontiveros B.B.F., Steichen M., State R., 2019. Mint Centrality: A Centrality Measure for the Bitcoin Transaction Graph. ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency, , pp.159-162.DOI: 10.1109/BLOC.2019.8751401
  • De Figueiredo B.C.B., Nakamura F.G., Nakamura E.F., 2021. A group-based centrality for undirected multiplex networks: a case study of the Brazilian Car Wash Operation. IEEE Access, .DOI: 10.1109/ACCESS.2021.3086027
  • Zhao J., Song Y., Deng Y., 2020. A novel model to identify the influential nodes: Evidence theory centrality. IEEE Access, 8, pp.46773-46780.DOI: 10.1109/ACCESS.2020.2978142
  • Lv L., Zhang K., Zhang T., Li X., Zhang J., Xue W., 2019. Eigenvector centrality measure based on node similarity for multilayer and temporal networks. IEEE Access, 7, pp.115725-115733.DOI: 10.1109/ACCESS.2019.2936217
  • Zhou H., Ruan M., Zhu C., Leung V.C.M., Xu S., Huang C.M., 2018. A Time-Ordered Aggregation Model-Based Centrality Metric for Mobile Social Networks. IEEE Access, 6, pp.25588-25599.DOI: 10.1109/ACCESS.2018.2831247
  • Ibrahim M.H., Missaoui R., Vaillancourt J., 2020. Cross-Face Centrality: A New Measure for Identifying Key Nodes in Networks Based on Formal Concept Analysis. IEEE Access, 8, pp.206901-206913.DOI: 10.1109/ACCESS.2020.3038306
  • Cai B., Li X., Gao Y., 2020. An Efficient Trust Inference Algorithm with Local Weighted Centrality for Social Recommendation. IEEE International Conference on Communications, 2020-June.DOI: 10.1109/ICC40277.2020.9149325
  • Carrizosa E., Marin A., Pelegrin M., 2020. Spotting Key Members in Networks: Clustering-Embedded Eigenvector Centrality. IEEE Systems Journal, 14(3), pp.3916-3925.DOI: 10.1109/JSYST.2020.2982266
  • Wang P., Yu X., Lü J., 2014. Identification and evolution of structurally dominant nodes in protein-protein interaction networks. IEEE Transactions on Biomedical Circuits and Systems, 8(1), pp.87-97.DOI: 10.1109/TBCAS.2014.2303160
  • Syarif A., Abouaissa A., Idoumghar L., Lorenz P., Schott R., Staples G., 2019. New path centrality based on operator calculus approach for wireless sensor network deployment. IEEE Transactions on Emerging Topics in Computing, 7(1), pp.162-173.DOI: 10.1109/TETC.2016.2585045
  • Faghani M., Nguyen U., 2013. A study of xss worm propagation and detection mechanisms in online social networks. IEEE Transactions on Information Forensics and Security, 8(11), pp.1815-1826.DOI: 10.1109/TIFS.2013.2280884
  • He X., Gao M., Kan M.Y., Wang D., 2017. BiRank: Towards Ranking on Bipartite Graphs. IEEE Transactions on Knowledge and Data Engineering, 29(1), pp.57-71.DOI: 10.1109/TKDE.2016.2611584
  • Stai E., Sotiropoulos K., Karyotis V., Papavassiliou S., 2017. Hyperbolic Embedding for Efficient Computation of Path Centralities and Adaptive Routing in Large-Scale Complex Commodity Networks. IEEE Transactions on Network Science and Engineering, 4(3), pp.140-153.DOI: 10.1109/TNSE.2017.2690258
  • De Medeiros D.S.V., Campista M.E.M., Mitton N., De Amorim M.D., Pujolle G., 2017. The Power of Quasi-Shortest Paths: ρ-Geodesic Betweenness Centrality. IEEE Transactions on Network Science and Engineering, 4(3), pp.187-200.DOI: 10.1109/TNSE.2017.2708705
  • Li, M., Lu, Y., Niu, Z. and Wu, F.X., 2017. United Complex Centrality for Identification of Essential Proteins from PPI Networks. IEEE/ACM transactions on computational biology and bioinformatics, 14(2), pp.370-380.DOI: 10.1109/TCBB.2015.2394487
  • Tang X., Wang J., Zhong J., Pan Y., 2014. Predicting essential proteins basedon weighted degree centrality. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(2), pp.407-418.DOI: 10.1109/TCBB.2013.2295318
  • Wang J., Li M., Wang H., Pan Y., 2012. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(4), pp.1070-1080.DOI: 10.1109/TCBB.2011.147
  • Qi, Y. and Luo, J., 2015. Prediction of essential proteins based on local interaction density. IEEE/ACM transactions on computational biology and bioinformatics, 13(6), pp.1170-1182.DOI: 10.1109/TCBB.2015.2509989
  • Zhang W., Xu J., Li Y., Zou X., 2018. Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(1), pp.109-116.DOI: 10.1109/TCBB.2016.2615931
  • Li G., Li M., Wang J., Li Y., Pan Y., 2020. United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4), pp.1451-1458.DOI: 10.1109/TCBB.2018.2889978
  • Gao, S. and Caines, P.E., 2018, July. Consensus-induced Centrality for Networks of Dynamical Systems. In Proceedings of the 23rd International Symposium on Mathematical Theory of Networks and Systems, Hong Kong, China (pp. 769-775).
  • Wąs, T., Rahwan, T. and Skibski, O., 2019, July. Random Walk Decay Centrality. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 2197-2204).DOI: 10.1609/aaai.v33i01.33012197
  • Vega-Oliveros D.A., Gomes P.S., E. Milios E., Berton L., 2019. A multi-centrality index for graph-based keyword extraction. Information Processing and Management, 56(6).DOI: 10.1016/j.ipm.2019.102063
  • Qi X., Fuller E., Wu Q., Wu Y., Zhang C., 2012. Laplacian centrality: A new centrality measure for weighted networks. Information Sciences, 194, pp.240-253.DOI: 10.1016/j.ins.2011.12.027
  • Zhao J., Wang P., Lui J.C.S., Towsley D., Guan X., 2017. I/O-efficient calculation of H-group closeness centrality over disk-resident graphs. Information Sciences, 400-401, pp.105-128.DOI: 10.1016/j.ins.2017.03.017
  • Saito K., Kimura M., Ohara K., Motoda H., 2016. Super mediator - A new centrality measure of node importance for information diffusion over social network. Information Sciences, 329, pp.985-1000.DOI: 10.1016/j.ins.2015.03.034
  • Lulli A., Ricci L., Carlini E., Dazzi P., 2015. Distributed Current Flow Betweenness Centrality. International Conference on Self-Adaptive and Self-Organizing Systems, SASO, 2015-October, pp.71-80.DOI: 10.1109/SASO.2015.15
  • Adebayo I.G., Sun Y., 2020. A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid. International Journal of Emerging Electric Power Systems, 21(3).DOI: 10.1515/ijeeps-2020-0013
  • Li Y., Sheng Y., Ye X., 2020. Group centrality algorithms based on the h-index for identifying influential nodes in large-scale networks. International Journal of Innovative Computing, Information and Control, 16(4), pp.1183-1201.DOI: 10.24507/ijicic.16.04.1183
  • Lu P., Dong C., 2019. Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure. International Journal of Modern Physics B, 33(32).DOI: 10.1142/S0217979219503958
  • Lu P., Yu J.J., 2020. A mixed clustering coefficient centrality for identifying essential proteins. International Journal of Modern Physics B, 34(10).DOI: 10.1142/S0217979220500903
  • Ma Y., Liu M., Zhang P., Qi X., 2018. CS-TOTR: A new vertex centrality method for directed signed networks based on status theory. International Journal of Modern Physics C, 29(5).DOI: 10.1142/S0129183118400028
  • Senturk I.F., 2019. Partition-aware centrality measures for connectivity restoration in mobile sensor networks. International Journal of Sensor Networks, 30(1), pp.1-12.DOI: 10.1504/IJSNET.2019.099218
  • Forouzandeh S., Sheikhahmadi A., Rezaei Aghdam A., Xu S., 2018. New centrality measure for nodes based on user social status and behavior on Facebook. International Journal of Web Information Systems, 14(2), pp.158-176.DOI: 10.1108/IJWIS-07-2017-0053
  • Yazici, M. and Sarac, M., 2015. Centrality measures with a new index called E-User (Effective User) Index for determiningthe most effective user in Twitter Online Social Network. International Journal on Computer Science and Engineering, 7(1), p.1.
  • Agryzkov T., Pedroche F., Tortosa L., Vicent J.F., 2018. Combining the two-layers pageRank approach with the APA centrality in networks with data. ISPRS International Journal of Geo-Information, 7(12).DOI: 10.3390/ijgi7120480
  • Luo J., Zhang N., 2014. Prediction of Essential Proteins Based On Edge Clustering Coefficient and Gene Ontology Information. Journal of Biological Systems, 22(3), pp.339-351.DOI: 10.1142/S0218339014500119
  • Sun H., Liang Y., Chen L., Wang Y., Du W., Shi X., 2013. An improved sum of edge clustering coefficient method for essential protein identification. Journal of Bionanoscience, 7(4), pp.386-390.DOI: 10.1166/jbns.2013.1152
  • Jensen P., Morini M., Karsai M., Venturini T., Vespignani A., Jacomy M., Cointet J.P., Mercklé P., Fleury E., 2016. Detecting global bridges in networks. Journal of Complex Networks, 4(3), pp.319-329.DOI: 10.1093/comnet/cnv022
  • Arrigo F., Grindrod P., Higham D., Noferini V., 2018. Non-backtracking walk centrality for directed networks. Journal of Complex Networks, 6(1), pp.54-78.DOI: 10.1093/comnet/cnx025
  • Castro N., Stella M., 2019. The multiplex structure of the mental lexicon influences picture naming in people with aphasia. Journal of Complex Networks, 7(6), pp.913-931.DOI: 10.1093/comnet/cnz012
  • Şimşek M., Meyerhenke H., 2020. Combined centrality measures for an improved characterization of influence spread in social networks. Journal of Complex Networks, 8(1).DOI: 10.1093/comnet/cnz048
  • Criado R., Flores J., García E., del Amo A.J.G., Pérez Á., Romance M., 2019. On the α-nonbacktracking centrality for complex networks: Existence and limit cases. Journal of Computational and Applied Mathematics, 350, pp.35-45.DOI: 10.1016/j.cam.2018.09.048
  • Avrachenkov K., Borkar V., Nemirovsky D., 2010. Quasi-stationary distributions as centrality measures for the giant strongly connected component of a reducible graph. Journal of Computational and Applied Mathematics, 234(11), pp.3075-3090.DOI: 10.1016/j.cam.2010.02.001
  • Wang, Y., Chen, G. 2013, A centrality measure based on two layer neighbors for complex networks. 9: 1 (2013) 25–32.
  • Kaur M., Singh S., 2017. Ranking based comparative analysis of graph centrality measures to detect negative nodes in online social networks. Journal of Computational Science, 23, pp.91-108.DOI: 10.1016/j.jocs.2017.10.018
  • Huang X., Huang W., 2019. Eigenedge: A measure of edge centrality for big graph exploration. Journal of Computer Languages, 55.DOI: 10.1016/j.cola.2019.100925
  • Saito, K., Fushimi, T., Ohara, K., Kimura, M. and Motoda, H., Efficient computation of target-oriented link criticalness centrality in uncertain graphs.
  • Donato C., Lo Giudice P., Marretta R., Ursino D., Virgili L., 2019. A well-tailored centrality measure for evaluating patents and their citations. Journal of Documentation, 75(4), pp.750-772.DOI: 10.1108/JD-10-2018-0168
  • Zhao S., Rousseau R., Ye F., 2011. H-Degree as a basic measure in weighted networks. Journal of Informetrics, 5(4), pp.668-677.DOI: 10.1016/j.joi.2011.06.005
  • Zhai L., Yan X., Zhang G., 2018. Bi-directional h-index: A new measure of node centrality in weighted and directed networks. Journal of Informetrics, 12(1), pp.299-314.DOI: 10.1016/j.joi.2018.01.004
  • Everett M.G., Borgatti S.P., 1999. The centrality of groups and classes. Journal of Mathematical Sociology, 23(3), pp.181-201.DOI: 10.1080/0022250X.1999.9990219
  • Keng Y.Y., Kwa K.H., McClain C., 2020. Convex combinations of centrality measures. Journal of Mathematical Sociology, .DOI: 10.1080/0022250X.2020.1765776
  • Jian, X., 2014. KSC centralized index model in complex network. Journal of Networks, 9(5), p.1245.
  • Ibnoulouafi A., El Haziti M., Cherifi H., 2018. M-Centrality: Identifying key nodes based on global position and local degree variation. Journal of Statistical Mechanics: Theory and Experiment, 2018(7).DOI: 10.1088/1742-5468/aace08
  • Sotoodeh H., Falahrad M., 2019. Relative Degree Structural Hole Centrality, CRD−SH: A New Centrality Measure in Complex Networks. Journal of Systems Science and Complexity, 32(5), pp.1306-1323.DOI: 10.1007/s11424-018-7331-5
  • Kleinberg J.M., 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), pp.604-632.DOI: 10.1145/324133.324140
  • Bavelas A., 1950. Communication Patterns in Task-Oriented Groups. Journal of the Acoustical Society of America, 22(6), pp.725-730.DOI: 10.1121/1.1906679
  • Lv L., Zhang K., Bardou D., Zhang T., Cai Y., 2019. A new centrality measure based on topologically biased random walks for multilayer networks. Journal of the Physical Society of Japan, 88(2).DOI: 10.7566/JPSJ.88.024010
  • Koschützki D., Schwöbbermeyer H., Schreiber F., 2007. Ranking of network elements based on functional substructures. Journal of Theoretical Biology, 248(3), pp.471-479.DOI: 10.1016/j.jtbi.2007.05.038
  • Vogiatzis, C. and Camur, M.C., 2019. Identification of essential proteins using induced stars in protein–protein interaction networks. INFORMS Journal on Computing, 31(4), pp.703-718.DOI: 10.1287/ijoc.2018.0872
  • De Meo P., Ferrara E., Fiumara G., Ricciardello A., 2012. A novel measure of edge centrality in social networks. Knowledge-Based Systems, 30, pp.136-150.DOI: 10.1016/j.knosys.2012.01.007
  • Riquelme F., Gonzalez-Cantergiani P., Molinero X., Serna M., 2018. Centrality measure in social networks based on linear threshold model. Knowledge-Based Systems, 140, pp.92-102.DOI: 10.1016/j.knosys.2017.10.029
  • Zareie A., Sheikhahmadi A., Jalili M., Fasaei M.S.K., 2020. Finding influential nodes in social networks based on neighborhood correlation coefficient. Knowledge-Based Systems, 194.DOI: 10.1016/j.knosys.2020.105580
  • Brandes U., Fleischer D., 2005. Centrality measures based on current flow. Lecture Notes in Computer Science, 3404, pp.533-544.DOI: 10.1007/978-3-540-31856-9_44
  • Ortiz-Arroyo D., Hussain D., 2008. An information theory approach to identify sets of key players. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5376 LNCS, pp.15-26.DOI: 10.1007/978-3-540-89900-6_5
  • Chen C., Wang W., Wang X., 2016. Efficient maximum closeness centrality group identification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9877 LNCS, pp.43-55.DOI: 10.1007/978-3-319-46922-5_4
  • Wang Q., Yu X., Zhang X., 2013. A connectionist model-based approach to centrality discovery in social networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8178 LNAI, pp.82-94.DOI: 10.1007/978-3-319-04048-6_8
  • Fushimi T., Satoh T., Saito K., Kazama K., Kando N., 2016. Content centrality measure for networks: Introducing distance-based Decay weights. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10047 LNCS, pp.40-54.DOI: 10.1007/978-3-319-47874-6_4
  • Kundu S., Murthy C., Pal S., 2011. A new centrality measure for influence maximization in social networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6744 LNCS, pp.242-247.DOI: 10.1007/978-3-642-21786-9_40
  • Avrachenkov K., Litvak N., Medyanikov V., Sokol M., 2013. Alpha current flow betweenness centrality. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8305 LNCS, pp.106-117.DOI: 10.1007/978-3-319-03536-9_9
  • Avrachenkov K., Mazalov V., Tsynguev B., 2015. Beta current flow centrality for weighted networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9197, pp.216-227.DOI: 10.1007/978-3-319-21786-4_19
  • Lockhart J., Minello G., Rossi L., Severini S., Torsello A., 2016. Edge centrality via the Holevo quantity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10029 LNCS, pp.143-152.DOI: 10.1007/978-3-319-49055-7_13
  • Rossi L., Torsello A., 2017. Measuring vertex centrality using the Holevo quantity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10310 LNCS, pp.154-164.DOI: 10.1007/978-3-319-58961-9_14
  • Mazalov V., Tsynguev B., 2016. Kirchhoff centrality measure for collaboration network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9795, pp.147-157.DOI: 10.1007/978-3-319-42345-6_13
  • Wang H., Li M., Wang J., Pan Y., 2011. A new method for identifying essential proteins based on edge clustering coefficient. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6674 LNBI, pp.87-98.DOI: 10.1007/978-3-642-21260-4_12
  • Oliva G., Esposito Amideo A., Starita S., Setola R., Scaparra M.P., 2020. Aggregating centrality rankings: A novel approach to detect critical infrastructure vulnerabilities. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11777 LNCS, pp.57-68.DOI: 10.1007/978-3-030-37670-3_5
  • Lyu T., Sun F., Zhang Y., 2020. Node Conductance: A Scalable Node Centrality Measure on Big Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12085 LNAI, pp.529-541.DOI: 10.1007/978-3-030-47436-2_40
  • Singh A., Singh R., Iyengar S., 2019. Hybrid centrality measures for service coverage problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11917 LNCS, pp.81-94.DOI: 10.1007/978-3-030-34980-6_11
  • Yao Y., Xiao X., Zhang C., Xia S., 2018. Classifying quality centrality for source localization in social networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10966 LNCS, pp.295-307.DOI: 10.1007/978-3-319-94289-6_19
  • Riveros C., Salas J., 2020. A family of centrality measures for graph data based on subgraphs. Leibniz International Proceedings in Informatics, LIPIcs, 155.DOI: 10.4230/LIPIcs.ICDT.2020.23
  • Kanwar K., Kaushal S., Kumar H., 2019. A hybrid node ranking technique for finding influential nodes in complex social networks. Library Hi Tech, .DOI: 10.1108/LHT-01-2019-0019
  • Jacobsen K., Tien J., 2018. A generalized inverse for graphs with absorption. Linear Algebra and Its Applications, 537, pp.118-147.DOI: 10.1016/j.laa.2017.09.029
  • Fei L., Mo H., Deng Y., 2017. A new method to identify influential nodes based on combining of existing centrality measures. Modern Physics Letters B, 31(26).DOI: 10.1142/S0217984917502438
  • Lu P., Dong C., 2020. EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity. Modern Physics Letters B, 34(26).DOI: 10.1142/S021798492050284X
  • Karabekmez M., Kirdar B., 2016. A novel topological centrality measure capturing biologically important proteins. Molecular BioSystems, 12(2), pp.666-673.DOI: 10.1039/C5MB00732A
  • Del Sol A., Fujihashi H., Amoros D., Nussinov R., 2006. Residues crucial for maintaining short paths in network communication mediate signaling in proteins. Molecular Systems Biology, 2.DOI: 10.1038/msb4100063
  • Coutinho R., Boukerche A., Vieira L., Loureiro A., 2016. A novel centrality metric for topology control in underwater sensor networks. MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, , pp.205-212.DOI: 10.1145/2988287.2989162
  • De Domenico M., Solé-Ribalta A., Omodei E., Gómez S., Arenas A., 2015. Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6.DOI: 10.1038/ncomms7868
  • Reiffers-Masson A., Labatut V., 2017. Opinion-based centrality in multiplex networks: A convex optimization approach. Network Science, 5(2), pp.213-234.DOI: 10.1017/nws.2017.7
  • Li X., Liu Y., Jiang Y., Liu X., 2016. Identifying social influence in complex networks: A novel conductance eigenvector centrality model. Neurocomputing, 210, pp.141-154.DOI: 10.1016/j.neucom.2015.11.123
  • Ding C., Li K., 2018. Centrality ranking in multiplex networks using topologically biased random walks. Neurocomputing, 312, pp.263-275.DOI: 10.1016/j.neucom.2018.05.109
  • Lin C., Chin C., Wu H., Chen S., Ho C., Ko M., 2008. Hubba: hub objects analyzer--a framework of interactome hubs identification for network biology.. Nucleic acids research, 36(Web Server issue).DOI: 10.1093/nar/gkn257
  • De la Cruz Cabrera O., Matar M., Reichel L., 2021. Centrality measures for node-weighted networks via line graphs and the matrix exponential. Numerical Algorithms, .DOI: 10.1007/s11075-020-01050-0
  • Negahban S., Oh S., Shah D., 2017. Rank centrality: Ranking from pairwise comparisons. Operations Research, 65(1), pp.266-287.DOI: 10.1287/opre.2016.1534
  • Xu Y., Feng Z., Qi X., 2021. Signless-laplacian eigenvector centrality: A novel vital nodes identification method for complex networks. Pattern Recognition Letters, 148, pp.7-14.DOI: 10.1016/j.patrec.2021.04.018
  • Qi X., Fuller E., Luo R., Zhang C.Q., 2015. A novel centrality method for weighted networks based on the Kirchhoff polynomial. Pattern Recognition Letters, 58, pp.51-60.DOI: 10.1016/j.patrec.2015.02.007
  • Salavaty, Abbas and Ramialison, Mirana and Currie, Peter D., IHS; An Integrative Method for the Identification of Network Hubs. Available at SSRN: https://ssrn.com/abstract=3565980 or http://dx.doi.org/10.2139/ssrn.3565980 DOI: 10.2139/ssrn.3565980
  • Salavaty, A., Ramialison, M. and Currie, P.D., 2020. Integrated value of influence: an integrative method for the identification of the most influential nodes within networks. Patterns, 1(5), p.100052.DOI: 10.1016/j.patter.2020.100052
  • Oggier F., Phetsouvanh S., Datta A., 2019. A split-and-transfer flow based entropic centrality. PeerJ Computer Science, 2019(9).DOI: 10.7717/peerj-cs.220
  • Estrada E., Higham D.J., Hatano N., 2009. Communicability betweenness in complex networks. Physica A: Statistical Mechanics and its Applications, 388(5), pp.764-774.DOI: 10.1016/j.physa.2008.11.011
  • Bae J., Kim S., 2014. Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Physica A: Statistical Mechanics and its Applications, 395, pp.549-559.DOI: 10.1016/j.physa.2013.10.047
  • Dangalchev C., 2006. Residual closeness in networks. Physica A: Statistical Mechanics and its Applications, 365(2), pp.556-564.DOI: 10.1016/j.physa.2005.12.020
  • Korn A., Schubert A., Telcs A., 2009. Lobby index in networks. Physica A: Statistical Mechanics and its Applications, 388(11), pp.2221-2226.DOI: 10.1016/j.physa.2009.02.013
  • Marchiori M., Latora V., 2000. Harmony in the small-world. Physica A: Statistical Mechanics and its Applications, 285(3), pp.539-546.DOI: 10.1016/S0378-4371(00)00311-3
  • Singh R., Chakraborty A., Manoj B., 2017. GFT centrality: A new node importance measure for complex networks. Physica A: Statistical Mechanics and its Applications, 487, pp.185-195.DOI: 10.1016/j.physa.2017.06.018
  • Nie T., Guo Z., Zhao K., Lu Z., 2016. Using mapping entropy to identify node centrality in complex networks. Physica A: Statistical Mechanics and its Applications, 453, pp.290-297.DOI: 10.1016/j.physa.2016.02.009
  • Ma Q., Ma J., 2017. Identifying and ranking influential spreaders in complex networks with consideration of spreading probability. Physica A: Statistical Mechanics and its Applications, 465, pp.312-330.DOI: 10.1016/j.physa.2016.08.041
  • Chen D., Lü L., Shang M., Zhang Y., Zhou T., 2012. Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), pp.1777-1787.DOI: 10.1016/j.physa.2011.09.017
  • Pu C., Cui W., Yang J., 2014. Tunable path centrality: Quantifying the importance of paths in networks. Physica A: Statistical Mechanics and its Applications, 405, pp.267-277.DOI: 10.1016/j.physa.2014.03.039
  • Estrada E., Hatano N., 2010. A vibrational approach to node centrality and vulnerability in complex networks. Physica A: Statistical Mechanics and its Applications, 389(17), pp.3648-3660.DOI: 10.1016/j.physa.2010.03.030
  • Wang J., Hou X., Li K., Ding Y., 2017. A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks. Physica A: Statistical Mechanics and its Applications, 475, pp.88-105.DOI: 10.1016/j.physa.2017.02.007
  • Li Q., Zhou T., Lü L., Chen D., 2014. Identifying influential spreaders by weighted LeaderRank. Physica A: Statistical Mechanics and its Applications, 404, pp.47-55.DOI: 10.1016/j.physa.2014.02.041
  • Kumar S., Panda B.S., 2020. Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach. Physica A: Statistical Mechanics and its Applications, 553.DOI: 10.1016/j.physa.2020.124215
  • Ni C., Yang J., Kong D., 2020. Sequential seeding strategy for social influence diffusion with improved entropy-based centrality. Physica A: Statistical Mechanics and its Applications, 545.DOI: 10.1016/j.physa.2019.123659
  • Sun H.l., Chen D.b., He J.l., Ch\'ng E., 2019. A voting approach to uncover multiple influential spreaders on weighted networks. Physica A: Statistical Mechanics and its Applications, 519, pp.303-312.DOI: 10.1016/j.physa.2018.12.001
  • Ma L.L., Ma C., Zhang H.F., Wang B.H., 2016. Identifying influential spreaders in complex networks based on gravity formula. Physica A: Statistical Mechanics and its Applications, 451, pp.205-212.DOI: 10.1016/j.physa.2015.12.162
  • Salavati C., Abdollahpouri A., Manbari Z., 2018. BridgeRank: A novel fast centrality measure based on local structure of the network. Physica A: Statistical Mechanics and its Applications, 496, pp.635-653.DOI: 10.1016/j.physa.2017.12.087
  • Liu H., Ma C., Xiang B., Tang M., Zhang H., 2018. Identifying multiple influential spreaders based on generalized closeness centrality. Physica A: Statistical Mechanics and its Applications, 492, pp.2237-2248.DOI: 10.1016/j.physa.2017.11.138
  • Chen X., Tan M., Zhao J., Yang T., Wu D., Zhao R., 2019. Identifying influential nodes in complex networks based on a spreading influence related centrality. Physica A: Statistical Mechanics and its Applications, 536.DOI: 10.1016/j.physa.2019.122481
  • Zareie A., Sheikhahmadi A., 2019. EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks. Physica A: Statistical Mechanics and its Applications, 514, pp.141-155.DOI: 10.1016/j.physa.2018.09.064
  • Li X., Zhou S., Liu J., Lian G., Chen G., Lin C.W., 2019. Communities detection in social network based on local edge centrality. Physica A: Statistical Mechanics and its Applications, 531.DOI: 10.1016/j.physa.2019.121552
  • Ma Y., Cao Z., Qi X., 2019. Quasi-Laplacian centrality: A new vertex centrality measurement based on Quasi-Laplacian energy of networks. Physica A: Statistical Mechanics and its Applications, 527.DOI: 10.1016/j.physa.2019.121130
  • Lv L., Zhang K., Bardou D., Zhang T., Zhang J., Cai Y., Jiang T., 2019. A new centrality measure based on random walks for multilayer networks under the framework of tensor computation. Physica A: Statistical Mechanics and its Applications, 526.DOI: 10.1016/j.physa.2019.04.236
  • Dai Z., Li P., Chen Y., Zhang K., Zhang J., 2019. Influential node ranking via randomized spanning trees. Physica A: Statistical Mechanics and its Applications, 526.DOI: 10.1016/j.physa.2019.02.047
  • Joseph A., Chen G., 2014. Composite centrality: A natural scale for complex evolving networks. Physica D: Nonlinear Phenomena, 267, pp.58-67.DOI: 10.1016/j.physd.2013.08.005
  • Izaac J.A., Zhan X., Bian Z., Wang K., Li J., Wang J.B., Xue P., 2017. Centrality measure based on continuous-time quantum walks and experimental realization. Physical Review A, 95(3).DOI: 10.1103/PhysRevA.95.032318
  • Izaac J.A., Wang J.B., Abbott P.C., Ma X.S., 2017. Quantum centrality testing on directed graphs via PT-symmetric quantum walks. Physical Review A, 96(3).DOI: 10.1103/PhysRevA.96.032305
  • Iannelli F., Mariani M., Sokolov I., 2018. Influencers identification in complex networks through reaction-diffusion dynamics. Physical Review E, 98(6).DOI: 10.1103/PhysRevE.98.062302
  • Zhang, Y., Shao, C., He, S. and Gao, J., 2020. Resilience centrality in complex networks. Physical Review E, 101(2), p.022304.DOI: 10.1103/PhysRevE.101.022304
  • Liu A., Porter M.A., 2020. Spatial strength centrality and the effect of spatial embeddings on network architecture. Physical Review E, 101(6).DOI: 10.1103/PhysRevE.101.062305
  • Estrada E., Rodríguez-Velázquez J.A., 2005. Spectral measures of bipartivity in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 72(4).DOI: 10.1103/PhysRevE.72.046105
  • Nepusz T., Petróczi A., Négyessy L., Bazsó F., 2008. Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 77(1).DOI: 10.1103/PhysRevE.77.016107
  • Newman M.E.J., 2006. Finding community structure in networks using the eigenvectors of matrices. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 74(3).DOI: 10.1103/PhysRevE.74.036104
  • Ghosh R., Lerman K., 2011. Parameterized centrality metric for network analysis. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 83(6).DOI: 10.1103/PhysRevE.83.066118
  • Ercsey-Ravasz M., Lichtenwalter R.N., Chawla N.V., Toroczkai Z., 2012. Range-limited centrality measures in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 85(6).DOI: 10.1103/PhysRevE.85.066103
  • Estrada E., Rodríguez-Velázquez J.A., 2005. Subgraph centrality in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 71(5).DOI: 10.1103/PhysRevE.71.056103
  • Newman M.E.J., Girvan M., 2004. Finding and evaluating community structure in networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 69(2 2).DOI: 10.1103/PhysRevE.69.026113
  • Martin T., Zhang X., Newman M.E.J., 2014. Localization and centrality in networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 90(5).DOI: 10.1103/PhysRevE.90.052808
  • Goh K., Kahng B., Kim D., 2001. Universal Behavior of Load Distribution in Scale-Free Networks. Physical Review Letters, 87(27), pp.278701-278701-4.DOI: 10.1103/PhysRevLett.87.278701
  • Noh J., Rieger H., 2004. Random Walks on Complex Networks. Physical Review Letters, 92(11).DOI: 10.1103/PhysRevLett.92.118701
  • Guo L., Zhang W.Y., Luo Z.J., Gao F.J., Zhang Y.C., 2017. A dynamical approach to identify vertices′ centrality in complex networks. Physics Letters, Section A: General, Atomic and Solid State Physics, 381(48), pp.3972-3977.DOI: 10.1016/j.physleta.2017.10.033
  • Chen G., Zhou S., Liu J., Li M., Zhou Q., 2020. Influential node detection of social networks based on network invulnerability. Physics Letters, Section A: General, Atomic and Solid State Physics, 384(34).DOI: 10.1016/j.physleta.2020.126879
  • Yang F., Li X., Xu Y., Liu X., Wang J., Zhang Y., Zhang R., Yao Y., 2018. Ranking the spreading influence of nodes in complex networks: An extended weighted degree centrality based on a remaining minimum degree decomposition. Physics Letters, Section A: General, Atomic and Solid State Physics, 382(34), pp.2361-2371.DOI: 10.1016/j.physleta.2018.05.032
  • Shanahan M., Wildie M., 2012. Knotty-centrality: Finding the connective core of a complex network. PLoS ONE, 7(5).DOI: 10.1371/journal.pone.0036579
  • Richters O., Peixoto T., 2011. Trust transitivity in social networks. PLoS ONE, 6(4).DOI: 10.1371/journal.pone.0018384
  • Chen D.B., Gao H., Lü L., Zhou T., 2013. Identifying influential nodes in large-scale directed networks: The role of clustering. PLoS ONE, 8(10).DOI: 10.1371/journal.pone.0077455
  • Zhang X., Xu J., Xiao W.x., 2013. A New Method for the Discovery of Essential Proteins. PLoS ONE, 8(3).DOI: 10.1371/journal.pone.0058763
  • Piraveenan M., Prokopenko M., Hossain L., 2013. Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks. PLoS ONE, 8(1).DOI: 10.1371/journal.pone.0053095
  • Liu Y., Slotine J., Barabási A., 2012. Control Centrality and Hierarchical Structure in Complex Networks. PLoS ONE, 7(9).DOI: 10.1371/journal.pone.0044459
  • Joyce K., Laurienti P., Burdette J., Hayasaka S., 2010. A new measure of centrality for brain networks. PLoS ONE, 5(8).DOI: 10.1371/journal.pone.0012200
  • Wang Y., Sun H., Du W., Blanzieri E., Viero G., Xu Y., Liang Y., 2014. Identification of essential proteins based on ranking Edge-Weights in Protein-Protein Interaction networks. PLoS ONE, 9(9).DOI: 10.1371/journal.pone.0108716
  • Mistry D., Wise R.P., Dickerson J.A., 2017. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network. PLoS ONE, 12(11).DOI: 10.1371/journal.pone.0187091
  • Youm Y., Lee B., Kim J., 2021. A measure of centrality in cyclic diffusion processes: Walk-betweenness. PLoS ONE, 16(1 January).DOI: 10.1371/journal.pone.0245476
  • Lü L., Zhang Y., Yeung C., Zhou T., 2011. Leaders in social networks, the delicious case. PLoS ONE, 6(6).DOI: 10.1371/journal.pone.0021202
  • Wang P., Lü J., Yu X., 2014. Identification of important nodes in directed biological networks: A network motif approach. PLoS ONE, 9(8).DOI: 10.1371/journal.pone.0106132
  • Szalay K., Csermely P., 2013. Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool. PLoS ONE, 8(10).DOI: 10.1371/journal.pone.0078059
  • Duron C., 2020. Heatmap centrality: A new measure to identify super-spreader nodes in scale-free networks. PLoS ONE, 15(7 July).DOI: 10.1371/journal.pone.0235690
  • Fronzetti Colladon A., Naldi M., 2020. Distinctiveness centrality in social networks. PLoS ONE, 15(5).DOI: 10.1371/journal.pone.0233276
  • Simko G., Csermely P., 2013. Nodes Having a Major Influence to Break Cooperation Define a Novel Centrality Measure: Game Centrality. PLoS ONE, 8(6).DOI: 10.1371/journal.pone.0067159
  • Milenković T., Memišević V., Bonato A., Pržulj N., 2011. Dominating biological networks. PLoS ONE, 6(8).DOI: 10.1371/journal.pone.0023016
  • Wang Y., Wang S., Deng Y., 2019. A modified efficiency centrality to identify influential nodes in weighted networks. Pramana - Journal of Physics, 92(4).DOI: 10.1007/s12043-019-1727-1
  • Natale F., Savini L., Giovannini A., Calistri P., Candeloro L., Fiore G., 2011. Evaluation of risk and vulnerability using a Disease Flow Centrality measure in dynamic cattle trade networks. Preventive Veterinary Medicine, 98(2-3), pp.111-118.DOI: 10.1016/j.prevetmed.2010.11.013
  • Jackson, M. O. 2008. Social and economic networks, volume 3. Princeton university press.
  • Plana F., Perez J., 2019. QuickCent: A Fast and Frugal Heuristic for Centrality Estimation on Networks. Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, (), pp.238-245.DOI: 10.1109/WI.2018.00-84
  • De Meo P., Levene M., Provetti A., 2019. Potential gain as a centrality measure. Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, , pp.418-422.DOI: 10.1145/3350546.3352559
  • Aleskerov F., Roman A., Rezyapova A., Yakuba V., 2020. New Centrality Measures in Networks and their Applications to the International Trade and Migration Networks. Proceedings - IEEE Computer Society\'s Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS, 2020-November.DOI: 10.1109/MASCOTS50786.2020.9285957
  • Roohi L., Rubinstein B.I.P., Teague V., 2019. Differentially-Private Two-Party Egocentric Betweenness Centrality. Proceedings - IEEE INFOCOM, 2019-April(), pp.2233-2241.DOI: 10.1109/INFOCOM.2019.8737405
  • Mavroforakis C., Mathioudakis M., Gionis A., 2016. Absorbing random-walk centrality: Theory and algorithms. Proceedings - IEEE International Conference on Data Mining, ICDM, 2016-January, pp.901-906.DOI: 10.1109/ICDM.2015.103
  • MacKer J., 2016. An improved local bridging centrality model for distributed network analytics. Proceedings - IEEE Military Communications Conference MILCOM, , pp.600-605.DOI: 10.1109/MILCOM.2016.7795393
  • Kamvar S., Schlosser M., Garcia-Molina H., 2003. The EigenTrust algorithm for reputation management in P2P networks. Proceedings of the 12th International Conference on World Wide Web, WWW 2003, , pp.640-651.DOI: 10.1145/775152.775242
  • Ovelgönne M., Kang C., Sawant A., Subrahmanian V., 2012. Covertness centrality in networks. Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, , pp.863-870.DOI: 10.1109/ASONAM.2012.156
  • Cheng Y., Lee R., Lim E., Zhu F., 2013. DelayFlow centrality for identifying critical nodes in transportation networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, , pp.1462-1463.DOI: 10.1145/2492517.2492595
  • Lee T., Lee H., Hwang K., 2013. Identifying superspreaders for epidemics using R0-adjusted network centrality. Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013, , pp.2239-2249.DOI: 10.1109/WSC.2013.6721600
  • Ghanem M., Coriat F., Tabourier L., 2017. Ego-betweenness centrality in link streams. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, , pp.667-674.DOI: 10.1145/3110025.3110158
  • Yi, Y., Shan, L., Li, H. and Zhang, Z., 2018, July. Biharmonic distance related centrality for edges in weighted networks. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 3620-3626).
  • Wang Z., Pei X., Wang Y., Yao Y., 2017. Ranking the key nodes with temporal degree deviation centrality on complex networks. Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, , pp.1484-1489.DOI: 10.1109/CCDC.2017.7978752
  • Alahakoon T., Tripathi R., Kourtellis N., Simha R., Iamnitchi A., 2011. K-path centrality: A new centrality measure in social networks. Proceedings of the 4th Workshop on Social Network Systems, SNS\'11, .DOI: 10.1145/1989656.1989657
  • Hwang W., Kim T., Ramanathan M., Zhang A., 2008. Bridging centrality: Graph mining from element level to group level. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp.336-344.DOI: 10.1145/1401890.1401934
  • White S., Smyth P., 2003. Algorithms for estimating relative importance in networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp.266-275.DOI: 10.1145/956750.956782
  • Deng H., Lyu M., King I., 2009. A generalized Co-HITS algorithm and its application to bipartite graphs. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp.239-247.DOI: 10.1145/1557019.1557051
  • Shah, D. and Zaman, T., 2010, June. Detecting sources of computer viruses in networks: theory and experiment. In Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems (pp. 203-214).DOI: 10.1145/1811039.1811063
  • Li H., Zhang Z., 2018. Kirchhoff index as a measure of edge centrality in weighted networks: Nearly linear time algorithms. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, , pp.2377-2396.DOI: 10.1137/1.9781611975031.153
  • Chanekar P.V., Nozari E., Cortes J., 2019. Network Modification using a Novel Gramian-based Edge Centrality. Proceedings of the IEEE Conference on Decision and Control, 2019-December, pp.1686-1691.DOI: 10.1109/CDC40024.2019.9028860
  • Barrat A., Barthélemy M., Pastor-Satorras R., Vespignani A., 2004. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11), pp.3747-3752.DOI: 10.1073/pnas.0400087101
  • Kleinberg, J.M., 1998, January. Authoritative sources in a hyperlinked environment. In Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms (pp. 668-677).
  • Wang G., Shen Y., Luan E., 2008. Measure of centrality based on modularity matrix. Progress in Natural Science, 18(8), pp.1043-1047.DOI: 10.1016/j.pnsc.2008.03.015
  • Katz L., 1953. A new status index derived from sociometric analysis. Psychometrika, 18(1), pp.39-43.DOI: 10.1007/BF02289026
  • Li J., Dueñas-Osorio L., Chen C., Shi C., 2017. AC power flow importance measures considering multi-element failures. Reliability Engineering and System Safety, 160, pp.89-97.DOI: 10.1016/j.ress.2016.11.010
  • Allouch, N., Meca, A. and Polotskaya, K., 2021. The Bonacich Shapley centrality. School of Economics, University of Kent.
  • Maslov S., Sneppen K., 2002. Specificity and stability in topology of protein networks. Science, 296(5569), pp.910-913.DOI: 10.1126/science.1065103
  • Fu L., Gao L., Ma X., 2010. A centrality measure based on spectral optimization of modularity density. Science in China, Series F: Information Sciences, 53(9), pp.1727-1737.DOI: 10.1007/s11432-010-4043-4
  • Liu J., Lin J., Guo Q., Zhou T., 2016. Locating influential nodes via dynamics-sensitive centrality. Scientific Reports, 6.DOI: 10.1038/srep21380
  • Xu S., Wang P., Lü J., 2017. Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks. Scientific Reports, 7.DOI: 10.1038/srep41321
  • Madotto A., Liu J., 2016. Super-Spreader Identification Using Meta-Centrality. Scientific Reports, 6.DOI: 10.1038/srep38994
  • Wang Z., Dueñas-Osorio L., Padgett J., 2015. A new mutually reinforcing network node and link ranking algorithm. Scientific Reports, 5.DOI: 10.1038/srep15141
  • Huang D., Yu Z., 2017. Dynamic-Sensitive centrality of nodes in temporal networks. Scientific Reports, 7.DOI: 10.1038/srep41454
  • Shao H., Mesbahi M., Li D., Xi Y., 2017. Inferring centrality from network snapshots. Scientific Reports, 7.DOI: 10.1038/srep40642
  • Zhang J., Chen D., Dong Q., Zhao Z., 2016. Identifying a set of influential spreaders in complex networks. Scientific Reports, 6.DOI: 10.1038/srep27823
  • Amano S., Ogawa K., Miyake Y., 2018. Node property of weighted networks considering connectability to nodes within two degrees of separation. Scientific Reports, 8(1).DOI: 10.1038/s41598-018-26781-y
  • Giustolisi O., Ridolfi L., Simone A., 2020. Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics. Scientific Reports, 10(1).DOI: 10.1038/s41598-020-60151-x
  • Lawyer G., 2015. Understanding the influence of all nodes in a network. Scientific Reports, 5.DOI: 10.1038/srep08665
  • Ghalmane Z., Cherifi C., Cherifi H., Hassouni M.E., 2019. Centrality in Complex Networks with Overlapping Community Structure. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-46507-y
  • Zhou X., Liang X., Zhao J., Zhang S., 2018. Cycle Based Network Centrality. Scientific Reports, 8(1).DOI: 10.1038/s41598-018-30249-4
  • Kwon H., Choi Y.H., Lee J.M., 2019. A Physarum Centrality Measure of the Human Brain Network. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-42322-7
  • Park J., Hescott B.J., Slonim D.K., 2019. Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-42299-3
  • Zaoli S., Mazzarisi P., Lillo F., 2019. Trip Centrality: walking on a temporal multiplex with non-instantaneous link travel time. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-47115-6
  • Zhang G., Liu L., Feng Y., Shao Z., Li Y., 2014. Cext-N index: a network node centrality measure for collaborative relationship distribution. Scientometrics, 101(1), pp.291-307.DOI: 10.1007/s11192-014-1358-8
  • Zhang, W., 2016. Screening node attributes that significantly influence node centrality in the network. Selforganizology, 3(3), pp.75-86.
  • Torres, L., Chan, K.S., Tong, H. and Eliassi-Rad, T., 2021. Nonbacktracking Eigenvalues under Node Removal: X-Centrality and Targeted Immunization. SIAM Journal on Mathematics of Data Science, 3(2), pp.656-675.DOI: 10.1137/20M1352132
  • Fontugne R., Shah A., Aben E., 2017. AS hegemony: A robust metric for as centrality. SIGCOMM Posters and Demos 2017 - Proceedings of the 2017 SIGCOMM Posters and Demos, Part of SIGCOMM 2017, , pp.48-50.DOI: 10.1145/3123878.3131982
  • Britt B.C., Hayes J.L., Musaev A., Sheinidashtegol P., Parrott S., Albright D.L., 2021. Using targeted betweenness centrality to identify bridges to neglected users in the Twitter conversation on veteran suicide. Social Network Analysis and Mining, 11(1).DOI: 10.1007/s13278-021-00747-x
  • Taheri S.M., Mahyar H., Firouzi M., Ghalebi E., Grosu R., Movaghar A., 2017. HellRank: a Hellinger-based centrality measure for bipartite social networks. Social Network Analysis and Mining, 7(1).DOI: 10.1007/s13278-017-0440-7
  • Bonacich P., Lloyd P., 2001. Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3), pp.191-201.DOI: 10.1016/S0378-8733(01)00038-7
  • Freeman L., 1978. Centrality in social networks conceptual clarification. Social Networks, 1(3), pp.215-239.DOI: 10.1016/0378-8733(78)90021-7
  • Kolaczyk E., Chua D., Barthélemy M., 2009. Group betweenness and co-betweenness: Inter-related notions of coalition centrality. Social Networks, 31(3), pp.190-203.DOI: 10.1016/j.socnet.2009.02.003
  • Hage P., Harary F., 1995. Eccentricity and centrality in networks. Social Networks, 17(1), pp.57-63.DOI: 10.1016/0378-8733(94)00248-9
  • Borgatti S., Everett M., 2006. A Graph-theoretic perspective on centrality. Social Networks, 28(4), pp.466-484.DOI: 10.1016/j.socnet.2005.11.005
  • Valente T., Foreman R., 1998. Integration and radiality: Measuring the extent of an individual\'s connectedness and reachability in a network. Social Networks, 20(1), pp.89-105.DOI: 10.1016/S0378-8733(97)00007-5
  • Everett M., Borgatti S.P., 2005. Ego network betweenness. Social Networks, 27(1), pp.31-38.DOI: 10.1016/j.socnet.2004.11.007
  • Everett M., Borgatti S., 2012. Categorical attribute based centrality: E-I and G-F centrality. Social Networks, 34(4), pp.562-569.DOI: 10.1016/j.socnet.2012.06.002
  • Freeman L., Borgatti S., White D., 1991. Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), pp.141-154.DOI: 10.1016/0378-8733(91)90017-N
  • Sinclair P., 2009. Network centralization with the Gil Schmidt power centrality index. Social Networks, 31(3), pp.214-219.DOI: 10.1016/j.socnet.2009.04.004
  • Seidman S., 1983. Network structure and minimum degree. Social Networks, 5(3), pp.269-287.DOI: 10.1016/0378-8733(83)90028-X
  • Smith J., Halgin D., Kidwell-Lopez V., Labianca G., Brass D., Borgatti S., 2014. Power in politically charged networks. Social Networks, 36(1), pp.162-176.DOI: 10.1016/j.socnet.2013.04.007
  • Everett M., Borgatti S., 2014. Networks containing negative ties. Social Networks, 38(1), pp.111-120.DOI: 10.1016/j.socnet.2014.03.005
  • Bonacich P., Lloyd P., 2004. Calculating status with negative relations. Social Networks, 26(4), pp.331-338.DOI: 10.1016/j.socnet.2004.08.007
  • Fan M., Cao Z., Cheng J., Yang F., Qi X., 2020. Degree-like centrality with structural zeroes or ones: When is a neighbor not a neighbor?. Social Networks, 63, pp.38-46.DOI: 10.1016/j.socnet.2020.05.002
  • Kumar R., Manuel S. (2019) A Centrality Measure for Directed Networks: m-Ranking Method. In: Özyer T., Bakshi S., Alhajj R. (eds) Social Networks and Surveillance for Society. Lecture Notes in Social Networks. Springer, Cham.DOI: 10.1007/978-3-319-78256-0_7
  • Freeman, Linton. 1977. A set of measures of centrality based on betweenness. Sociometry. 40 (1): 35–41.DOI: 10.2307/3033543
  • Clemente G.P., Cornaro A., 2020. A novel measure of edge and vertex centrality for assessing robustness in complex networks. Soft Computing, 24(18), pp.13687-13704.DOI: 10.1007/s00500-019-04470-w
  • Tavassoli S., Zweig K.A., 2017. Fuzzy centrality evaluation in complex and multiplex networks. Springer Proceedings in Complexity, , pp.31-43.DOI: 10.1007/978-3-319-54241-6_3
  • Puzis R., Sofer Z., Cohen D., Hugi M., 2018. Embedding-centrality: Generic centrality computation using neural networks. Springer Proceedings in Complexity, (219279), pp.87-97.DOI: 10.1007/978-3-319-73198-8_8
  • Aleskerov F., Andrievskaya I., Permjakova E., 2016. Key borrowers detected by the intensities of their short-range interactions. Springer Proceedings in Mathematics and Statistics, 156, pp.267-280.DOI: 10.1007/978-3-319-29608-1_18
  • Brandes, U., 2005. Network analysis: methodological foundations (Vol. 3418). Springer Science & Business Media.
  • Aleskerov, F.T., Meshcheryakova, N. and Shvydun, S., 2016. Centrality measures in networks based on nodes attributes, long-range interactions and group influence. Long-Range Interactions and Group Influence.DOI: 10.2139/ssrn.3196962
  • Sarmento R.P., Cordeiro M., Brazdil P., Gama J., 2018. Efficient incremental laplace centrality algorithm for dynamic networks. Studies in Computational Intelligence, 689, pp.341-352.DOI: 10.1007/978-3-319-72150-7_28
  • Fushimi T., Saito K., Ikeda T., Kazama K., 2019. A new group centrality measure for maximizing the connectedness of network under uncertain connectivity. Studies in Computational Intelligence, 812, pp.3-14.DOI: 10.1007/978-3-030-05411-3_1
  • Giscard P., Wilson R., 2018. Cycle-centrality in economic and biological networks. Studies in Computational Intelligence, 689, pp.14-28.DOI: 10.1007/978-3-319-72150-7_2
  • Gao L., Yu S., Li M., Shen Z., Gao Z., 2019. Weighted h-index for identifying influential spreaders. Symmetry, 11(10).DOI: 10.3390/sym11101263
  • Pedroche F., Tortosa L., Vicent J.F., 2019. An eigenvector centrality for multiplex networks with data. Symmetry, 11(6).DOI: 10.3390/sym11060763
  • Agryzkov T., Curado M., Pedroche F., Tortosa L., Vicent J.F., 2019. Extending the adapted PageRank algorithm centrality to multiplex networks with data using the PageRank two-layer approach. Symmetry, 11(2).DOI: 10.3390/sym11020284
  • Ranjan, G. and Zhang, Z.L., 2010. On random eccentricity in complex networks. Tech. Report.
  • Qiu, L., Zhang, J., Tian, X. and Zhang, S., 2021. Identifying Influential Nodes in Complex Networks Based on Neighborhood Entropy Centrality. The Computer Journal.DOI: 10.1093/comjnl/bxab034
  • Meghanathan, N., 2017. A computationally lightweight and localized centrality metric in lieu of betweenness centrality for complex network analysis. Vietnam Journal of Computer Science, 4(1), pp.23-38.DOI: 10.1007/s40595-016-0073-1
  • Wang Y., Chen B., Li W., Zhang D., 2019. Influential Node Identification in Command and Control Networks Based on Integral k-Shell. Wireless Communications and Mobile Computing, 2019.DOI: 10.1155/2019/6528431
  • Weng J., Lim E.P., Jiang J., He Q., 2010. TwitterRank: Finding topic-sensitive influential twitterers. WSDM 2010 - Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, , pp.261-270.DOI: 10.1145/1718487.1718520
  • Lyu L., Fain B., Munagala K., Wang K., 2021. Centrality with Diversity. WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, , pp.644-652.DOI: 10.1145/3437963.3441789
  • Han Z., Chen Y., Li M., Liu W., Yang W., 2016. An efficient node influence metric based on triangle in complex networks. Wuli Xuebao/Acta Physica Sinica, 65(16).DOI: 10.7498/aps.65.168901
  • Ruan Y., Lao S., Wang J., Bai L., Chen L., 2017. Node importance measurement based on neighborhood similarity in complex network. Wuli Xuebao/Acta Physica Sinica, 66(3).DOI: 10.7498/aps.66.038902