Community detection based on links and node features in social networks

© Springer International Publishing Switzerland 2015. Community detection is a significant but challenging task in the field of social network analysis. Many effective methods have been proposed to solve this problem. However, most of them are mainly based on the topological structure or node attributes. In this paper, based on SPAEM [1], we propose a joint probabilistic model to detect community which combines node attributes and topological structure. In our model, we create a novel feature-based weighted network, within which each edge weight is represented by the node feature similarity between two nodes at the end of the edge. Then we fuse the original network and the created network with a parameter and employ expectation-maximization algorithm (EM) to identify a community. Experiments on a diverse set of data, collected from Facebook and Twitter, demonstrate that our algorithm has achieved promising results compared with other algorithms

Scalable Text and Link Analysis with Mixed-Topic Link Models

Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.Comment: 11 pages, 4 figure