2011 ItsWhoYouKnowGraphMiningUsingRe

• (Henderson et al., 2011) ⇒ Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, and Christos Faloutsos. (2011). “It's Who You Know: Graph Mining Using Recursive Structural Features.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020512

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Cited By

• http://scholar.google.com/scholar?q=%222011%22+It%27s+Who+You+Know%3A+Graph+Mining+Using+Recursive+Structural+Features
• http://dl.acm.org/citation.cfm?id=2020408.2020512&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; design; experimentation; feature extraction; graph mining; identity resolution; network classification; performance

Abstract

Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize the other? The key step in all such graph mining tasks is to find effective node features. We propose ReFeX (Recursive Feature extraction), a novel algorithm, that recursively combines local (node-based) features with neighborhood (egonet-based) features; and outputs regional features - capturing "behavioral" information. We demonstrate how these powerful regional features can be used in within-network and across-network classification and de-anonymization tasks - without relying on homophily, or the availability of class labels. The contributions of our work are as follows: (a) ReFeX is scalable and (b) it is effective, capturing regional(" behavioral") information in large graphs. We report experiments on real graphs from various domains with over 1M edges, where ReFeX outperforms its competitors on typical graph mining tasks like network classification and de-anonymization.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ItsWhoYouKnowGraphMiningUsingRe	Christos Faloutsos Lei Li Leman Akoglu Hanghang Tong Keith Henderson Tina Eliassi-Rad Brian Gallagher			It's Who You Know: Graph Mining Using Recursive Structural Features				10.1145/2020408.2020512		2011