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.

中文翻译：

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一种新的基于信任模型的社交网络重叠社区检测算法

随着互联网技术的飞速发展，社交网络已经成为社交互动、生活方式展示和信息传播的主要平台。社交网络中有效的社区检测有助于评估公众情绪、识别社区领袖并产生个性化推荐。虽然文献中已经提出了不同的社区检测方法，但基于信任模型的检测方案将用户交互建模为信任转移，这有助于捕获网络中的隐含关系。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进行建模，因为这些用户在加入网络后的一段时间内几乎没有交互。在本文中，我们提出了 TLCDA，一种新的基于信任模型的社区检测算法。通过使用社交网络中的节点间关系强度和相似性来增强传统的信任计算，TLCDA 通过粗粒度 K-Mediods 聚类来检测社区。我们对真实社交网络的评估表明，由 TLCDA 检测到的社区在满足拓扑内聚力的同时表现出优越的偏好内聚力。