Influential Attributed Communities via Graph Convolutional Network (InfACom-GCN)

Community search is a basic problem in graph analysis. In many applications, network nodes have certain properties that are important for the community to make sense of the application; hence, attributes are associated with nodes to capture their properties. Community influence is an important community property that can be used to rank communities in a network based on the relevance/importance of a particular attribute. Unfortunately, most of the community search algorithms introduced previously in attributed networks research work ignored the community influence. When searching for influential communities, two potential data sources can be used: network attributes and nodes. Dealing with structure-related attributes is a challenge. Recently, the graph neural network (GNN) has completely changed the field of graph representation learning by effectively learning node embedding and has achieved the most advanced results in tasks such as node classification and connection prediction. In this paper, we investigate the problem of searching for the influential communities in attributed networks. We propose an efficient algorithm for retrieving the influential communities in a large attributed network. The proposed approach contains two main steps: (1) Community detection using a graph convolutional network in a semi-supervised learning setting considering the correlation between attributes and the overall graph information, and (2) constructing the influential communities resulting from step 1. The proposed approach is evaluated on various real datasets. The experimental results show the efficiency and effectiveness of the proposed implementations.