Ego-betweenness Centrality
Definition
It takes into account the temporal information, it does not demand the knowledge of the global structure of the network, it is computationally light and parameter-free.
$$C(e,\tau)={\underset{i,j\in N_e \times N_e}{\sum}} {p_{ij}(e,\tau)\over p_{ij}(\tau)} ,$$
where $p_{ij}(\tau)$ is the number of most recent paths of length at most 2 from $i$ to $j$ at time $\tau$ and $p_{ij}(e,\tau)$ is the number of such paths going through $e$.
It only takes into account the direct neighbors of a node to compute its centrality. This restriction allows to carry out the computation in a shorter time compared to a case where any couple of nodes in the network should be considered.
$$C(e,\tau)={\underset{i,j\in N_e \times N_e}{\sum}} {p_{ij}(e,\tau)\over p_{ij}(\tau)} ,$$
where $p_{ij}(\tau)$ is the number of most recent paths of length at most 2 from $i$ to $j$ at time $\tau$ and $p_{ij}(e,\tau)$ is the number of such paths going through $e$.
It only takes into account the direct neighbors of a node to compute its centrality. This restriction allows to carry out the computation in a shorter time compared to a case where any couple of nodes in the network should be considered.
References
- 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
