Technical gazette, Vol. 26 No. 6, 2019.
Original scientific paper
https://doi.org/10.17559/TV-20190712024843
Hierarchical Semantic Community Detection in Information Networks: A Complete Information Graph Approach
Guilan Shen*
; Beijing Union University, A3, Yanjingdongli, Chaoyang district, Beijing,100025, China
Jie Sun
; Beijing Union University, A3, Yanjingdongli, Chaoyang district, Beijing,100025, China
Yaohui Hao
; State Key Laboratory of Mathematical Engineering and Advanced Computing, 62 Science Avenue, Zhengzhou City, Henan Province 450001, China
Abstract
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.
Keywords
complete information graph; content attributes; information network; semantic hierarchical community
Hrčak ID:
228518
URI
Publication date:
27.11.2019.
Visits: 1.394 *