An Approach to Finding Similarity Between Two Community Graphs Using Graph Mining Techniques

Bapuji Rao,Saroja Nanda

doi:10.14569/ijacsa.2016.070563

Abstract

Graph similarity has studied in the fields of shape retrieval, object recognition, face recognition and many more areas. Sometimes it is important to compare two community graphs for similarity which makes easier for mining the reliable knowledge from a large community graph. Once the similarity is done then, the necessary mining of knowledge can be extracted from only one community graph rather than both which leads saving of time. This paper proposes an algorithm for similarity check of two community graphs using graph mining techniques. Since a large community graph is difficult to visualize, so compression is essential. This proposed method seems to be easier and faster while checking for similarity between two community graphs since the comparison is between the two compressed community graphs rather than the actual large community graphs.

Highlights

A graph arises in many situations like web graph of documents, a social network graph of friends, a road-map graph of cities
To evaluate the performance of the proposed algorithm, the authors have considered seven community graphs namely CG1 to CG7, where 1st community graph CG1 is considered as principle community graph for comparison with the remaining six community graphs for finding similarities
Graph similarity technique is helpful in the fields of shape retrieval, object recognition, face recognition and many more areas

Summary

Introduction

A graph arises in many situations like web graph of documents, a social network graph of friends, a road-map graph of cities. Graph mining has grown rapidly for the last two decades due to the number, and the size of graphs has been growing exponentially (with billions of nodes and edges), and from it, the authors want to extract much more complicated information. Graph similarity has numerous applications in social networks, image processing, biological networks, chemical compounds, and computer vision, and it has suggested many algorithms and similarity measures. Graph similarity is that "a node in one graph is similar to a node in another graph if their neighborhoods are similar" [1]

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Conclusion