Semi-Supervised Community Detection Using Structure and Size

Arjun Bakshi, Srinivasan Parthasarathy, Kannan Srinivasan

Abstract: In recent years there have been a few semi-supervised community detection approaches that use community membership information, or node metadata to improve their performance. However, communities have always been thought of as clique-like structures, while the idea of finding and leveraging other patterns in communities is relatively unexplored. Online social networks provide a corpus of real communities in large graphs which can be used to understand dataset specific community patterns. In this paper, we design a way to represent communities concisely in an easy to compute feature space. We design an efficient community detection algorithm that uses size and structural information of communities from a training set to find communities in the rest of the graph. We show that our approach achieves 10% higher F1 scores on average compared to several other methods on large real-world graph datasets, even when the training set is small.