Community Detection Using Diffusion Information

Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available that is not feasible in many cases. In this article, we focus on detecting communities by exploiting their diffusion information. To this end, we utilize the Conditional Random Fields (CRF) to discover the community structures. The proposed method, community diffusion (CoDi), does not require any prior knowledge about the network structure or specific properties of communities. Furthermore, in contrast to the structure-based community detection methods, this method is able to identify the hidden communities. The experimental results indicate considerable improvements in detecting communities based on accuracy, scalability, and real cascade information measures.

The code of the paper [Community Diffusion (CoDi)].

Here you can find sample data set and script to run the codes: Script for running the codes and a LFR network as a sample data set.

Please cite our paper:

M. Ramezani, A. Khodadadi, and H. R.Rabiee, Community Detection Using Diffusion Information, Transactions on Knowledge Discovery from Data (TKDD), 2018.

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