Inferring Web communities from link topology
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Authoritative sources in a hyperlinked environment
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WWW '03 Proceedings of the 12th international conference on World Wide Web
Unsupervised Link Discovery in Multi-relational Data via Rarity Analysis
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CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of connection subgraphs
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SPIN: mining maximal frequent subgraphs from graph databases
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IEEE Internet Computing
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The KOJAK group finder: connecting the dots via integrated knowledge-based and statistical reasoning
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Data & Knowledge Engineering
SISP: a new framework for searching the informative subgraph based on PSO
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Bisociative Knowledge Discovery
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Discovering patterns in graphs has long been an area of interest. In most approaches to such pattern discovery either quantitative anomalies, frequency of substructure or maximum flow is used to measure the interestingness of a pattern. In this paper we introduce heuristics that guide a subgraph discovery algorithm away from banal paths towards more "informative" ones. Given an RDF graph a user might pose a question of the form: "What are the most relevant ways in which entity X is related to entity Y?" the response to which is a subgraph connecting X to Y. We use our heuristics to discover informative subgraphs within RDF graphs. Our heuristics are based on weighting mechanisms derived from edge semantics suggested by the RDF schema. We present an analysis of the quality of the subgraphs generated with respect to path ranking metrics. We then conclude presenting intuitions about which of our weighting schemes and heuristics produce higher quality subgraphs.