Clustering and Associating Method of Dual Heterogeneous Communities in Location Based Social Networks

In recent years, community discovery in heterogeneous information networks, especially in location-based social networks(LBSN), has become an emerging research hotspot attracting more and more attentions. However, since presently most of the traditional community discovery studies in social networks only focus on homogeneous network structures, there exists one obvious drawback in these studies that they cannot effectively integrate the multi-mode entities and their multi-dimensional heterogeneity relations included in LBSN. Therefore, in order to overcome this challenging problem, this paper proposes a novel dual heterogeneous communities clustering and associating method, which is called CCAM to fully fuse with multi-mode entities and their multi-relations. The main idea of CCAM is below: firstly, in the upper social media layer of LBSN, this method measures the similarity of publishing document topics between users by means of information entropy, and further transforms the similar interests clustering problem into solving the objective function based on fuzzy clustering to identify the overlapping topic-based communities of users. Then, in the bottom geographical location layer, the bipartite graph between users and locations with check-ins relationships is converted to the hyper graph model, and the proposed hyper edge clustering approach based on graph partition is exploited to obtain the point of interest clusters about geographical features of users. Finally, the representation model for associating the upper topic-based communities and the bottom geographical location clusters is established via users' social relations in the middle user layer of LBSN, and the local optimal solution of association function for the dual heterogeneous clusters is gained by using stochastic gradient descent method. The experimental results in two real LBSN datasets such as Foursquare(NYC) and Yelp show that our proposed CCAM method can effectively fuse three types of relations in LBSN, for example, social relations of users, social media publishing relations, and users check-ins relations. Consequently, this method can accurately obtain the closely correlated dual heterogeneous clusters such as the user's interest clusters and geographical location clusters, which not only makes the external structural characteristic and the internal interest cohesive index better than some traditional community clustering algorithms, but also outperforms these algorithms at least 32% through measuring the mean average precision in the both field of online interest topics recommendation and offline point of interest recommendation. © 2020, Science Press. All right reserved.