2013 GuidedLearningforRoleDiscoveryG

• (Gilpin et al., 2013) ⇒ Sean Gilpin, Tina Eliassi-Rad, and Ian Davidson. (2013). “Guided Learning for Role Discovery (GLRD): Framework, Algorithms, and Applications.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487620

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Guided+Learning+for+Role+Discovery+%28GLRD%29%3A+Framework%2C+Algorithms%2C+and+Applications
• http://dl.acm.org/citation.cfm?id=2487575.2487620&preflayout=flat#citedby

Quotes

Author Keywords

• Constrained clustering; data mining; graph mining; graphs and networks; role discovery

Abstract

Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way. In contrast to community discovery, which finds groups of highly connected nodes, role discovery finds groups of nodes that share similar topological structure in the graph, and hence a common role (or function) such as being a broker or a periphery node. However, existing work so far is completely unsupervised, which is undesirable for a number of reasons. We provide an alternating least squares framework that allows convex constraints to be placed on the role discovery problem, which can provide useful supervision. In particular we explore supervision to enforce i) sparsity, ii) diversity, and iii) alternativeness in the roles. We illustrate the usefulness of this supervision on various data sets and applications.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 GuidedLearningforRoleDiscoveryG	Ian Davidson Tina Eliassi-Rad Sean Gilpin			Guided Learning for Role Discovery (GLRD): Framework, Algorithms, and Applications				10.1145/2487575.2487620		2013