Revisiting Role Discovery in Networks: From Node to Edge Roles
This work addresses the gap in network analysis for edge roles, which could benefit researchers and practitioners in fields like social network analysis or bioinformatics, though it appears incremental by extending node-centric methods to edges.
The paper tackles the problem of discovering edge roles in networks, which previous work had not addressed, and presents a framework that automatically learns and extracts edge roles and features from arbitrary graphs, demonstrating utility across various domains.
Previous work in network analysis has focused on modeling the mixed-memberships of node roles in the graph, but not the roles of edges. We introduce the edge role discovery problem and present a generalizable framework for learning and extracting edge roles from arbitrary graphs automatically. Furthermore, while existing node-centric role models have mainly focused on simple degree and egonet features, this work also explores graphlet features for role discovery. In addition, we also develop an approach for automatically learning and extracting important and useful edge features from an arbitrary graph. The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.