Simultaneous detection of communities and roles from large networks

Community detection and structural role detection are two distinct but closely-related perspectives in network analytics. In this paper, we propose RC-Joint, a novel algorithm to simultaneously identify community and structural role assignments in a network. Rather than being agnostic to one assignment while inferring the other, RC-Joint employs a principled approach to guide the detection process in a nonparametric fashion and ensures that the two sets of assignments are sufficiently different from each other. Roles and communities generated by RC-Joint are both soft assignments, reflecting the fact that many real-world networks have overlapping community structures and role memberships. By comparing with state-of-the-art methods in community detection and structural role detection, we demonstrate that RC-Joint harvests the best of two worlds and outperforms existing approaches, while still being competitive in efficiency. We also investigate the effect of different initialization schemes, and find that using the results of RC-Joint on a sparse network as the seed often leads to faster convergence and higher quality.