Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:APPLIED INTELLIGENCE
Key Words:Social network analysis Machine learning Community discovery Bayesian network Ego network
Abstract: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 No.:0924-669X
Translation or Not:no
Date of Publication:2018-09-01
Co-author:Ding, Fei
Correspondence Author:zy
Date of Publication:2018-09-01