2016/10/04 by Nesreen K. Ahmed, Ahmed, Nesreen K., Ryan A. Rossi +5
Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Graph theory and applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI) #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.48550/arxiv.1610.00844
openalex publication_date 2016/10/04 · arxiv created 2016/11/07 · arxiv updated 2016/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.