Item sets based graph mining algorithm and application in genetic regulatory networks

Abstract

This paper presents some graph-mining algorithms and their application in genetic regulatory networks. We use item set labeling method of directed graphs, to efficiently find frequent subgraphs. An inexact match scheme of subgraphs is performed by an edit distance of vertices and directed edges in the Regulatory Networks. This inexact frequent subgraph algorithm extends the paper of Koyuturk, Grama and Szpankowski on exact frequent subgraphs (The Kayuturk et al paper considers the inexact match as an open problem).