Community Detection in Online Social Network Using Graph Embedding and Hierarchical Clustering

摘要

The community detection plays an important role in social network analysis. It can be used to find users that behave in a similar manner, detect groups of interests, cluster users in e-commerce application such as their taste or shopping habits, etc. In this paper, we proposed an algorithm to detect the community in online social networks. Our algorithm represents the nodes and the relationships in the social networks using a vector, agglomerative clustering (the most famous clustering algorithm) will cluster those vectors to figure out the communities. The experimental results show that our algorithm performs better traditional agglomerative clustering because of the ability to detect the community which has better modularity value.