CentiServer - Centrality

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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
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
Wang, Y., Chen, G. 2013, A centrality measure based on two layer neighbors for complex networks. 9: 1 (2013) 25–32.
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
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
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
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 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
Gurfinkel, A.J. and Rikvold, P.A., 2020. A Current-Flow Centrality With Adjustable Reach. arXiv preprint arXiv:2005.14356.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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 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
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
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
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
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
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
Katz L., 1953. A new status index derived from sociometric analysis. Psychometrika, 18(1), pp.39-43.DOI: 10.1007/BF02289026
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
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
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
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
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
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
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
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
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
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
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
Freeman, Linton. 1977. A set of measures of centrality based on betweenness. Sociometry. 40 (1): 35–41.DOI: 10.2307/3033543
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Pedroche F., Tortosa L., Vicent J.F., 2019. An eigenvector centrality for multiplex networks with data. Symmetry, 11(6).DOI: 10.3390/sym11060763
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
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
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
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
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
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
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
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).
Kleinberg J.M., 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), pp.604-632.DOI: 10.1145/324133.324140
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
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
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
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).
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
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
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
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
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
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.
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
Freeman L., 1978. Centrality in social networks conceptual clarification. Social Networks, 1(3), pp.215-239.DOI: 10.1016/0378-8733(78)90021-7
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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).
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
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
Keng Y.Y., Kwa K.H., McClain C., 2020. Convex combinations of centrality measures. Journal of Mathematical Sociology, .DOI: 10.1080/0022250X.2020.1765776
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
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
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
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
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
Zhou X., Liang X., Zhao J., Zhang S., 2018. Cycle Based Network Centrality. Scientific Reports, 8(1).DOI: 10.1038/s41598-018-30249-4
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
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
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
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
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
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
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
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
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
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
Stella M., De Domenico M., 2018. Distance entropy cartography characterises centrality in complex networks. Entropy, 20(4).DOI: 10.3390/e20040268
Fronzetti Colladon A., Naldi M., 2020. Distinctiveness centrality in social networks. PLoS ONE, 15(5).DOI: 10.1371/journal.pone.0233276
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
Milenković T., Memišević V., Bonato A., Pržulj N., 2011. Dominating biological networks. PLoS ONE, 6(8).DOI: 10.1371/journal.pone.0023016
Huang D., Yu Z., 2017. Dynamic-Sensitive centrality of nodes in temporal networks. Scientific Reports, 7.DOI: 10.1038/srep41454
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
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
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
Saito, K., Fushimi, T., Ohara, K., Kimura, M. and Motoda, H., Efficient computation of target-oriented link criticalness centrality in uncertain graphs.
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
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
Everett M., Borgatti S.P., 2005. Ego network betweenness. Social Networks, 27(1), pp.31-38.DOI: 10.1016/j.socnet.2004.11.007
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
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
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
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
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
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
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
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
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
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
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
Huang, S., Cui, H. and Ding, Y., 2014. Evaluation of node importance in complex networks. arXiv preprint arXiv:1402.5743.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Williams, J., 2019. Identifying sensitive components in infrastructure networks via critical flows. engrXiv.
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
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
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
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
Shao H., Mesbahi M., Li D., Xi Y., 2017. Inferring centrality from network snapshots. Scientific Reports, 7.DOI: 10.1038/srep40642
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
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
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
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
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
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
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
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
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
Xu S., Wang P., Lü J., 2017. Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks. Scientific Reports, 7.DOI: 10.1038/srep41321
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
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
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
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
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
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
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
Jian, X., 2014. KSC centralized index model in complex network. Journal of Networks, 9(5), p.1245.
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
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
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
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
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
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
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
Piraveenan M., Prokopenko M., Zomaya A., 2008. Local assortativeness in scale-free networks. EPL, 84(2).DOI: 10.1209/0295-5075/84/28002
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
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
Liu J., Lin J., Guo Q., Zhou T., 2016. Locating influential nodes via dynamics-sensitive centrality. Scientific Reports, 6.DOI: 10.1038/srep21380
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
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
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
Herzog S.M., Hills T.T., 2019. Mediation Centrality in Adversarial Policy Networks. Complexity, 2019.DOI: 10.1155/2019/1918504
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
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.
Magelinski, T., Bartulovic, M. and Carley, K.M., 2020. Modularity-Impact: a Signed Group Centrality Measure for Complex Networks. arXiv preprint arXiv:2003.00056.
Ivanov S., Gorlushkina N., Ivanova L., 2018. Multi-parametric centrality method for graph network models. AIP Conference Proceedings, 1952.DOI: 10.1063/1.5032005
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
Brandes, U., 2005. Network analysis: methodological foundations (Vol. 3418). Springer Science & Business Media.
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
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
Seidman S., 1983. Network structure and minimum degree. Social Networks, 5(3), pp.269-287.DOI: 10.1016/0378-8733(83)90028-X
Everett M., Borgatti S., 2014. Networks containing negative ties. Social Networks, 38(1), pp.111-120.DOI: 10.1016/j.socnet.2014.03.005
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
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
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
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
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.
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
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
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
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
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
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
Ranjan, G. and Zhang, Z.L., 2010. On random eccentricity in complex networks. Tech. Report.
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
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
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
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
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
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
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
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
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
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
Bonacich, P., 1987. Power and centrality: A family of measures. American journal of sociology, 92(5), pp.1170-1182.DOI: 10.1086/228631
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
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
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
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
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
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
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
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.
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
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
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
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
Noh J., Rieger H., 2004. Random Walks on Complex Networks. Physical Review Letters, 92(11).DOI: 10.1103/PhysRevLett.92.118701
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
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
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
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
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
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
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
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
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).
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
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
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
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
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
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
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
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
Zhang, W., 2016. Screening node attributes that significantly influence node centrality in the network. Selforganizology, 3(3), pp.75-86.
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
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
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
Jackson, M. O. 2008. Social and economic networks, volume 3. Princeton university press.
Euler, L., 1741. Solutio problematis ad geometriam situs pertinentis. Commentarii academiae scientiarum Petropolitanae, pp.128-140.
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
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
Maslov S., Sneppen K., 2002. Specificity and stability in topology of protein networks. Science, 296(5569), pp.910-913.DOI: 10.1126/science.1065103
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
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
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
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
Burt R., 2004. Structural holes and good ideas. American Journal of Sociology, 110(2), pp.349-399.DOI: 10.1086/421787
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
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
Madotto A., Liu J., 2016. Super-Spreader Identification Using Meta-Centrality. Scientific Reports, 6.DOI: 10.1038/srep38994
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
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
Allouch, N., Meca, A. and Polotskaya, K., 2021. The Bonacich Shapley centrality. School of Economics, University of Kent.
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
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
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
Ma X., Ma Y., 2019. The Local Triangle Structure Centrality Method to Rank Nodes in Networks. Complexity, 2019.DOI: 10.1155/2019/9057194
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
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
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
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
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
Richters O., Peixoto T., 2011. Trust transitivity in social networks. PLoS ONE, 6(4).DOI: 10.1371/journal.pone.0018384
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
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
Lawyer G., 2015. Understanding the influence of all nodes in a network. Scientific Reports, 5.DOI: 10.1038/srep08665
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
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
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
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
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
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
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