A Comparative Study on Community Detection Methods in Complex Networks

Community detection aims to discover cohesive groups in which people connect with each other closely in social networks. A variety of methods have been proposed to detect communities in social networks. However, there is still few work to make a comparative study on those methods. In this paper, we first introduce and compare several representative methods on community detection. Then we implement those methods with python and make a comparative analysis on different real world social networking data sets. The experimental results have shown that GN algorithm is suitable for small networks, while LPA algorithm has a better scalability. FU algorithm is of the best stability. This work could help researchers to understand the ideas of community detection methods better and select appropriate method on demand more easily

Communities and beyond: mesoscopic analysis of a large social network with complementary methods

Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.Comment: 11 pages, 10 figures. V2: typos corrected, one sentence added. V3: revised version, Appendix added. V4: final published versio

Community detection by label propagation with compression of flow

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm