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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：APPLIED INTELLIGENCE
关键字：Social network analysis Machine learning Community discovery Bayesian network Ego network
摘要：Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users' feature similarities and the causal dependencies between users' profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.
ISSN号：0924-669X
是否译文：否
发表时间：2018-09-01
合写作者：Ding, Fei
通讯作者：庄毅