Attributed social networks includes not only the network structure but also features of the nodes. The huge data in social networks has increased the risk of false discoveries, identifying communities in Facebook requires simple but effective fast techniques. Existing community detection methods suffer from high computational cost caused by both huge structure (nodes) and attribute dimensionality. Based on the homophily property of social networks, that linked nodes likely share similar attributes, we propose a new approach to rank attributes of a social network, called RELNA. Then we detect communities using only high ranked attributes instead of all, therefore, the computation time can be reduced dramatically. Comparisons between RELNA and LINKREC (a well-known attribute ranking algorithm) were made through experiments on Facebook data sets, the results are almost the same. Furthermore, we use the obtained top relevant attributes on CESNA algorithm (an well known overlapping community detection algorithm) to detect communities, results show that our approach is much faster meanwhile cluster precision keeps almost the same as CESNA.
['Bella Martinez-Seis', 'Xiaoou Li']
Ranking features in Facebook to detect overlapping communities