A Novel Trust Model Based Overlapping Community Detection Algorithm for Social Networks
A Novel Trust Model Based Overlapping Community Detection Algorithm for Social Networks
复制标题
一种基于信任模型的社交网络重叠社区检测算法
DOI：
10.1109/tkde.2019.2914201
复制
发表时间：
2020-11
影响因子：
8.9
通讯作者：
Youtao Zhang
中科院分区：
文献类型：
--
作者：
Shuai Ding;Zijie Yue;Shanlin Yang;Feng Niu;Youtao Zhang
随着互联网技术的快速发展，社交网络已经成为社交互动、生活方式展示和消息传播的主要平台。社交网络中有效的社区检测有助于评估公众情绪，识别社区领导者，并产生个性化推荐。虽然在文献中已经提出了不同的社区检测方法，基于信任模型的检测方案模型的用户交互作为信任转移，这有助于捕获网络中的隐式关系。不幸的是，基于信任模型的检测方案面临冷启动问题，即，它们不能准确地对新加入的用户建模，因为这些用户在加入网络之后的一段时间内几乎没有交互。提出了一种基于信任模型的社区发现算法TLCDA。TLCDA通过对社交网络中节点间关系强度和相似度的分析，改进了传统的信任计算方法，并通过粗粒度的K-Mediods聚类进行社区检测。对真实的社交网络的评价表明，TLCDA检测出的社区在满足拓扑内聚性的同时，表现出上级的偏好内聚性。
With the fast advances in Internet technologies, social networks have become a major platform for social interaction, lifestyle demonstration, and message dissemination. Effective community detection in social networks helps to assess public sentiment, identify community leaders, and produce personalized recommendation. While different community detection approaches have been proposed in the literature, the trust model based detection schemes model user interactions as trust transfer, which helps to capture the implicit relation in the network. Unfortunately, trust model based detection schemes face a cold start problem, i.e., they cannot accurately model newly joined users as these users have few interactions for a duration after joining the network. In this paper, we propose TLCDA, a novel trust model based community detection algorithm. By enhancing the traditional trust computation with inter-node relation strength and similarity in social networks, TLCDA detects communities through coarse-grained K-Mediods clustering. Our evaluation on real social networks shows that the communities detected by TLCDA exhibit superior preference cohesion while satisfying the topology cohesion.
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DOI：
10.1109/wiiat.2008.209
发表时间：
2008-12
期刊：
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/tkde.2016.2528249
发表时间：
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影响因子：
8.9
作者：
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通讯作者：
Yorke-Smith, Neil
DOI：
10.1109/tnnls.2016.2514368
发表时间：
2017-05-01
影响因子：
10.4
作者：
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通讯作者：
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影响因子：
5
作者：
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DOI：
10.4018/jdwm.2011100103
发表时间：
2011-10-01
影响因子：
1.2
作者：
Wang, Shuliang;Gan, Wenyan;Li, Deren
通讯作者：
Li, Deren