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(PDF) Identifying Social Communities in Complex ...
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In this paper, we demonstrate how identifying social communities can signiflcantly im- prove the forwarding e-ciencies in term of delivery ratio and delivery ...
Identifying Social Communities in Complex ...
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由 P Hui 著作2009被引用 20 次 — In this paper, we demonstrate how identifying social communities can significantly improve the forwarding efficiencies in term of delivery ratio and delivery ...
Identifying Social Communities in Complex ...
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Identifying Social Communities in Complex Communications for Network Efficiency ; 2009 · Lecture Notes of the Institute for Computer Sciences, Social Informatics ...
Identifying Social Communities in Complex Communications for ...
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Identifying Social Communities in Complex. Communications for Network Efficiency ... Identifying Social Communities for Network Efficiency. 5. Ranking. Source.
Identifying Social Communities in Complex ...
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由 P Hui 著作2012被引用 20 次 — In this paper, we demonstrate how identifying social communities can significantly improve the forwarding efficiencies in term of delivery ratio ...
[PDF] Identifying Social Communities in Complex ...
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It is demonstrated how identifying social communities can significantly improve the forwarding efficiencies in term of delivery ratio and delivery cost.
Identifying Social Communities in Complex Communications for ...
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Bibliographic details on Identifying Social Communities in Complex Communications for Network Efficiency.
Identifying and evaluating community structure in complex ...
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2024年10月22日 — We compare and evaluate different metrics for community structure in networks. In this context we also discuss a simple approach to ...
Community detection using local neighborhood in complex ...
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由 J Eustace 著作2015被引用 75 次 — Overlapping community detection, dynamic community identification and community evolution analysis are the three important problems for social network analysis.
Communities in Complex Networks
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由 A Arenas 著作被引用 3 次 — Introduction. 2. Definition of communities. 3. Evaluating community identification. 4. Link removal methods. 4.1. Shortest Path Centrality.
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