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Titlebook: Complex Networks & Their Applications IX; Volume 1, Proceeding Rosa M. Benito,Chantal Cherifi,Marta Sales-Pardo Conference proceedings 2021

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51#
發(fā)表于 2025-3-30 11:00:57 | 只看該作者
Towards Causal Explanations of Community Detection in Networksce - termed communities. Our goal is to further study this problem from a different perspective related to the questions of the cause of belongingness to a community. To this end, we apply the framework of causality and responsibility developed by Halpern and Pearl?[.]. We provide an algorithm-semi-
52#
發(fā)表于 2025-3-30 14:11:44 | 只看該作者
A Pledged Community? Using Community Detection to Analyze Autocratic Cooperation in UN Co-sponsorshiliable and do not sign agreements with them. While it is challenging to capture autocratic cooperation with traditional approaches such as signed alliance treaties, co-sponsorship at the United Nations General Assembly (UNGA) offers a valuable alternative. UNGA co-sponsorship is less binding than al
53#
發(fā)表于 2025-3-30 17:37:05 | 只看該作者
Distances on a Graphto the actual clustering, our goal is to find a distance whose pairwise minimization will lead to densely connected clusters. Our thesis is centered on the widely accepted notion that strong clusters are sets of vertices with high induced subgraph density. We posit that vertices sharing more connect
54#
發(fā)表于 2025-3-30 22:09:51 | 只看該作者
Local Community Detection Algorithm with Self-defining Source Nodesorithms. Considering the growing size of existing networks, . community detection methods have gained attention in contrast to . methods that impose a top-down view of global network information. Current local community detection algorithms are mainly aimed to discover local communities around a giv
55#
發(fā)表于 2025-3-31 01:00:31 | 只看該作者
Investigating Centrality Measures in Social Networks with Community Structurewith this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world
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