RolX: Structural role extraction & mining in large graphs

Keith Henderson; Brian Gallagher; Tina Eliassi-Rad; Hanghang Tong; Sugato Basu; Leman Akoglu; Danai Koutra; Christos Faloutsos; Lei Li

doi:10.1145/2339530.2339723

RolX: Structural role extraction & mining in large graphs

Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, Lei Li

Arizona State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

318 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages	1231-1239
Number of pages	9
DOIs	https://doi.org/10.1145/2339530.2339723
State	Published - Sep 14 2012
Event	18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China Duration: Aug 12 2012 → Aug 16 2012

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other	18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
Country/Territory	China
City	Beijing
Period	8/12/12 → 8/16/12

Keywords

graph mining
network classification
sense-making
similarity search
structural role discovery

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/2339530.2339723

Cite this

Henderson, K., Gallagher, B., Eliassi-Rad, T., Tong, H., Basu, S., Akoglu, L., Koutra, D., Faloutsos, C., & Li, L. (2012). RolX: Structural role extraction & mining in large graphs. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1231-1239). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2339530.2339723

RolX: Structural role extraction & mining in large graphs. / Henderson, Keith; Gallagher, Brian; Eliassi-Rad, Tina et al.
KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. p. 1231-1239 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Henderson, K, Gallagher, B, Eliassi-Rad, T, Tong, H, Basu, S, Akoglu, L, Koutra, D, Faloutsos, C & Li, L 2012, RolX: Structural role extraction & mining in large graphs. in KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1231-1239, 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, Beijing, China, 8/12/12. https://doi.org/10.1145/2339530.2339723

Henderson K, Gallagher B, Eliassi-Rad T, Tong H, Basu S, Akoglu L et al. RolX: Structural role extraction & mining in large graphs. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. p. 1231-1239. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/2339530.2339723

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AU - Akoglu, Leman

AU - Koutra, Danai

AU - Faloutsos, Christos

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N2 - 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.

AB - 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.

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RolX: Structural role extraction & mining in large graphs

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Keywords

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