Hierarchical Semantic Community Detection in Information Networks: A Complete Information Graph Approach

• Main Authors: Guilan Shen*, Jie Sun, Yaohui Hao
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2019-01-01
• Series: Tehnički Vjesnik
• Subjects: complete information graph; content attributes; information network; semantic hierarchical community
• Online Access: https://hrcak.srce.hr/file/332438

In order to detect the hierarchical semantic community which is helpful to discover the true organization of information network,we propose a complete information graph approach. In this method, we first use complete information graphs including semantic edges and link edges to represent information networks. Then we define semantic modularity as an objective function, a measure that can express not only the tightness of links, but also the consistency of content. Next, we improve Lovain's algorithm and propose simLV algorithm to detect communities on the complete information graph. This recursive algorithm itself can discover semantic communities of different sizes in the process of execution. Experiment results show the hierarchical community detected by the simLV algorithm performs better than the Louvain in measuring the consistency of semantic content for our approach takes into account the content attributes of nodes, which are neglected by many other methods. It can detect more meaningful community structures with consistent content and tight structure in information networks such as social networks, citation networks, web networks, etc., which is helpful to the application of information dissemination analysis, topic detection, public opinion detection, etc.