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本文集介绍了几种用于社区检测的研究方法和技术，包括基于博弈论的框架、适用于符号网络的谱方法、混合进化算法、密度基追踪框架及利用结构属性识别长期社交社区等。这些方法为理解和分析复杂网络中的社群结构提供了有力工具。

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ASONAM 12 ACCEPTED PAPER

A Game Theoretic Framework for Community Detection
Kishan Mehrotra*, Syracuse University; Jae Oh, Syracuse University; Patrick McSweeney, Syracsue
University

Communities and Balance in Signed Networks: A Spectral Approach
Pranay Anchuri*, RPI; Malik Magdon Ismail, Rensselaer Polytechnic Institute

A Hybrid Evolutionary Algorithm based on HSA and CLS for Multi-objective Community Detection in
Complex Networks
Babak Amiri*, The University of Sydney; Liaquat Hossain, ; John Crawford,

OCTracker: A Density-Based Framework for Tracking the Evolution of Overlapping Communities in OSNs
Sajid Bhat, Jamia Millia Islamia; Muhammad Abulaish*, King Saud University

Identifying long lived social communities using structural properties
James Thompson*, RPI; Malik Magdon Ismail, Rensselaer Polytechnic Institute; Mark Goldberg

Yasuhiro Yamada, Akira Hattori and Haruo Hayami, Method of Visualizing Relations between
Tweets to Facilitate Discussions via Twitter

Tian Zhang and Bin Wu, A Method for Local Community Detection by Finding Core Node

Mengjiao Wang, Donn Morrison and Conor Hayes, Early and Late Fusion Methods for the
Automatic Creation of Twitter Lists