2012/08/12 by Keith Henderson, Brian Gallagher, Tina Eliassi‐Rad +6 · 3 citations
Physics and Astronomy · Computer Science · Biochemistry, Genetics and Molecular Biology · #Complex Network Analysis Techniques #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks
paper · doi:10.1145/2339530.2339723
openalex publication_date 2012/08/12 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/29
Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, for example, "clique-members," "periphery-nodes," etc. Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching for similar nodes, and node classification. This paper addresses the question: Given a graph, how can we automatically discover roles for nodes? We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from general network data. We demonstrate the effectiveness of RolX on several network-mining tasks: from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not); and show that the two approaches are complimentary in nature.