Adaptive fusion of structure and attribute guided polarized communities search

In this paper, we propose the community search framework searching polarized communities via adaptively fusing structure and attribute in attributed signed networks, which searches for two polarized subgraphs on an attributed signed network for given query nodes. We first conduct a analysis by the similarity of attributes between nodes. And we adaptively integrate topology and node attributes into an augmented signed network. Then, a spectral method based on generalized Rayleigh quotient is proposed. Finally, a linear programming problem is designed to detect polarized communities by local eigenspace. Experiments on real-world datasets demonstrate the effectiveness of our method.