Performance Evaluation of Frequent Subgraph Discovery Techniques

Abstract EN

Due to rapid development of the Internet technology and new scientific advances, the number of applications that model the data as graphs increases, because graphs have highly expressive power to model a complicated structure.

Graph mining is a well-explored area of research which is gaining popularity in the data mining community.

A graph is a general model to represent data and has been used in many domains such as cheminformatics, web information management system, computer network, and bioinformatics, to name a few.

In graph mining the frequent subgraph discovery is a challenging task.

Frequent subgraph mining is concerned with discovery of those subgraphs from graph dataset which have frequent or multiple instances within the given graph dataset.

In the literature a large number of frequent subgraph mining algorithms have been proposed; these included FSG, AGM, gSpan, CloseGraph, SPIN, Gaston, and Mofa.

The objective of this research work is to perform quantitative comparison of the above listed techniques.

The performances of these techniques have been evaluated through a number of experiments based on three different state-of-the-art graph datasets.

This novel work will provide base for anyone who is working to design a new frequent subgraph discovery technique.