The hidden knowledge in the information network has attracted a large number of researchers from different subjects such as sociology, physics and computer science. Community discovery has great significance for the analysis of information network structure, the understanding of its function, the discovery of its hidden patterns, and the predication of its behavior. In the practical life, people tend to analyze the information network with a heuristic method, that is, analyze the partial structure which meets the specific needs abstracted from the huge amounts of relational data. For this case, a method of community discovery based on seeds expansion is put forward in this paper. The node that should be paid special attention to in the information network is called the seed node, and then nodes with high similarity with the seed node are added through the iterative way. Accepting the idea of clustering algorithm, this method can not only find its community according to the customization node, but also find the outlier nodes of the community. Experiments on the public test set and data set of Sina micro-blog have demonstrated the effectiveness of the method.

Microblogging is becoming a popular social media in recent years. Observations show that a large part of posts in microblogging were talking about public events occurred in the real world. Public concerns reflect interests and expectations of the mass for an event. Therefore, to understand and analyze of public concerns will help us to grasp an event, and predict its trend.This paper presents an evolution analysis method of public concerns for a special kind of post in microblogging, which can provides sufficient background information about an event by its attachments, e.g. a URL for details, a picture, or a video, etc. we called it expandable post. We use expandable posts to reconstruct the topic space. Their reposts are regarded as public concerns, and are located on the space. Thus, the task of tracking public concerns is transformed into tracking the movement of those reposts, and analyzing the relationships between them and their corresponding expandable posts on the topic space. The preliminary experiments on our dataset about H7N9 bird flu collected from Weibo, shows the effectiveness of our method.

Effective organization and management of a user's ego-network is one of the effective means to deal with information overload. Manual organization methods are often time-consuming and difficult to promote. In this paper, an automatic identification of social circles model is proposed, which takes three factors into account: user's profile, relationship information, and interactive context information. Using closed frequent itemsets mining on users' interactive context, a user in egonetwork can belong to more than one social circles or even no one. The experiments show that our model can effectively detect social circles on ego-network.

Revealing ideal community structure efficiently is very important for scientists from many fields. However, it is difficult to infer an ideal community division structure by only analyzing the topology information due to the increment and complication of the social network. Recent research on community detection uncovers that its performance could be improved by incorporating the node attribute information. Along this direction, this paper improves the Blondel–Guillaume–Lambiotte (BGL) method, which is a fast algorithm based on modularity maximization, by integrating the community attribute entropy. To fulfill this goal, our algorithm minimizes the community attribute entropy by removing the boundary nodes which are generated in the modularity maximization at each iteration. By this way, the communities detected by our algorithm make a balance between modularity maximization and community attribute entropy minimization. In addition, another merit of our algorithm is that it is free of parameters. Comprehensive experiments have been conducted on both artificial and real networks to compare the proposed community detection algorithm with several state-of-the-art ones. As the experimental results indicate, our algorithm demonstrates superior performance.

Event evolution analysis, which focus on discovering underlying relationships among events by using methods of data mining on text corpus, is a meaningful and challenge problem. In recent years, more and more people began to express their opinion on public events though microblogging services. It makes that the microblogging corpus contains not only the facts related to the events, but also the public concerns. Therefore, we believe that the event evolution analysis in microblogging should take different approaches and perspectives with the state-of-the-arts. In this paper, we employ the concept of public opinion field, on which event information and public opinion in text corpus are distinguished. Based on this view, we focus on how does the public opinion affect the evolution of events, propose a method to measure the influence, and represent it on the event evolution graph. The preliminary experiments on our dataset about H7N9 bird flu collected from Weibo shows that our method can get consistent results with our intuitive feel, that illustrates the effectiveness of the method.