方法证据记录
Dynamic Closeness Centrality
Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Dynamic Closeness Centrality in Temporal Networks
分类方法记录 · ml-model / network-analysis
- Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM. · DOI 10.1145/1852658.1852661
- Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. · DOI 10.1016/j.physrep.2012.03.001
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