Community Evolution Detection and Prediction in Online Social Network

In a social network, a group in which people are similar to each other or have tight connections form a community. There are many works trying to precisely detect communities in a social network. As the social network change from time to time, the communities in the social network keep changing. Detecting community’s evolution become a new topic in social network analysis. More and more papers are about finding new algorithms to detect or track communities’ evolution. But, we want to go further in this topic. We want to not only detect communities’ evolution but predict the communities’ evolution. This is a brand new problem, and we decide to take advantage of Weiux Lin’s algorithm (Long-term Evolution Method) to detect the communities’ evolution. We first generate our synthetic data to verify and analysis the algorithm. We apply Weiux’s algorithm on DBLP and Facebook dataset, and use our own defined evolution types to analysis the community evolution. We deeply discuss the community evolution in Facebook dataset, and find that the communities’ evolution can match to the events happening in Facebook. Finally, we use Libsvm, select enough features from our detecting data, and build a prediction model. The prediction result shows that our model performs pretty well, and the feature we selected help a lot to our prediction model. Besides, we compare our result with SGCI algorithm, which is a community evolution detection algorithm. The comparison shows the evolution types we defined and the algorithm we used are more accurate and more representative than SGCI.