GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases

Graphs are being increasingly used to model a wide range of scientific data. Such widespread usage of graphs has generated considerable interest in mining patterns from graph databases. While an array of techniques exists to mine frequent patterns, we still lack a scalable approach to mine statistically significant patterns, specifically patterns with low p-values, that occur at low frequencies. We propose a highly scalable technique, called GraphSig, to mine significant subgraphs from large graph databases. We convert each graph into a set of feature vectors where each vector represents a region within the graph. Domain knowledge is used to select a meaningful feature set. Prior probabilities of features are computed empirically to evaluate statistical significance of patterns in the feature space. Following analysis in the feature space, only a small portion of the exponential search space is accessed for further analysis. This enables the use of existing frequent subgraph mining tec...

• Type: Conference
• Year: 2009
• Where: ICDE
• Authors: Sayan Ranu, Ambuj K. Singh
• Keywords: Database, Frequent Patterns, Frequent Subgraph Mining, ICDE 2009, Large Graph Databases, Mining Patterns, Mining Significant Patterns