Overlapping community detection for count-value networks

QianCheng Yu, ZhiWen Yu, Zhu Wang, XiaoFeng Wang, YongZhi Wang
2019 Human-Centric Computing and Information Sciences  
Introduction Community detection is a fundamental problem in network analysis, as community structure which almost exists in all networks, is the most widely studied structural properties of networks. Statistical network generative model, due to its solid theoretical base, remarkable interpretability and relative tractability, has been wildly used for community detecting tasks [1] . Existing network generative models can be grouped into two classes: the latent class model, and the latent
more » ... model. The latent class model assume that each individual only affiliate with a single class (as show in Fig. 1a) . The latent feature model, increases the flexibility of the generative process by permitting each object possesses a vector of features and determine the link probabilities based on interactions among the features. In many real-world networks, communities are ordinarily overlapping rather than disjoint, so assuming that each object having hard membership in only one cluster became too restrict to consistent with the facts. An important challenge in community detection is to specify the number of communities in advance, as we do not have good prior knowledge of how many parameters the model requires to explain the data well. The relational infinite latent feature model Abstract Detecting network overlapping community has become a very hot research topic in the literature. However, overlapping community detection for count-value networks that naturally arise and are pervasive in our modern life, has not yet been thoroughly studied. We propose a generative model for count-value networks with overlapping community structure and use the Indian buffet process to model the community assignment matrix Z; thus, provide a flexible nonparametric Bayesian scheme that can allow the number of communities K to increase as more and more data are encountered instead of to be fixed in advance. Both collapsed and uncollapsed Gibbs sampler for the generative model have been derived. We conduct extensive experiments on simulated network data and real network data, and estimate the inference quality on single variable parameters. We find that the proposed model and inference procedure can bring us the desired experimental results. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
doi:10.1186/s13673-019-0202-9 fatcat:4yjzz5tpz5ckvjrrpfwiwub6fe