Discovering frequent induced subgraphs from directed networks

Document Type

Article

Publication Date

1-1-2018

Abstract

Directed networks find many applications in computer science, social science and biomedicine, among others. In this paper we propose a new graph mining algorithm that is capable of locating all frequent induced subgraphs in a given set of directed networks. We present an incremental coding scheme for representing the canonical form of a graph, study its properties, and develop new techniques for pattern generation suitable for directed networks. We prove that our algorithm is complete, meaning that no qualified pattern is missed by the algorithm. Furthermore, our algorithm is correct in the sense that all patterns found by the algorithm are frequent induced subgraphs in the given networks. Experimental results based on synthetic data and gene regulatory networks show the good performance of our algorithm, and its application in network inference.

Identifier

85058852579 (Scopus)

Publication Title

Intelligent Data Analysis

External Full Text Location

https://doi.org/10.3233/IDA-173681

e-ISSN

15714128

ISSN

1088467X

First Page

1279

Last Page

1296

Issue

6

Volume

22

Grant

2016ZD302

Fund Ref

State University of New York

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