2013 ApproximateGraphMiningwithLabel

• (Anchuri et al., 2013) ⇒ Pranay Anchuri, Mohammed J. Zaki, Omer Barkol, Shahar Golan, and Moshe Shamy. (2013). “Approximate Graph Mining with Label Costs.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487602

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Approximate+Graph+Mining+with+Label+Costs
• http://dl.acm.org/citation.cfm?id=2487575.2487602&preflayout=flat#citedby

Quotes

Author Keywords

• Approximate graph mining; approximate subgraph isomorphism; data mining; label costs

Abstract

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ApproximateGraphMiningwithLabel	Mohammed J. Zaki Pranay Anchuri Omer Barkol Shahar Golan Moshe Shamy			Approximate Graph Mining with Label Costs				10.1145/2487575.2487602		2013