A Parallel Community Detection Algorithm for Big Social Networks

Yathrib Alqahtani,Mourad Ykhlef

doi:10.14569/ijacsa.2018.090146

Abstract

Mining social networks has become an important task in data mining field, which describes users and their roles and relationships in social networks. Processing social networks with graph algorithms is the source for discovering many features. The most important algorithms applied to social networks are community detection algorithms. Communities of social networks are groups of people sharing common interests or activities. DenGraph is one of the density-based algorithms that used to find clusters of arbitrary shapes based on users’ interactions in social networks. However, because of the rapidly growing size of social networks, it is impossible to process a huge graph on a single machine in an acceptable level of execution. In this article, DenGraph algorithm has been redesigned to work in distributed computing environment. We proposed ParaDengraph Algorithm based on Pregel parallel model for large graph processing.

Highlights

Social networks have become extremely popular in the last years, and they have important roles in the dissemination of information and innovation
Edge Betweenness Clustering (EBC) [2] algorithms were applied to the same social network graph to compare both results and obtain the characteristics of both clustering methods
This article proposed ParaDengraph, a graph-parallel algorithm for community detection in large social networks based on the original sequential algorithm called DenGraph

Summary

Introduction

Social networks have become extremely popular in the last years, and they have important roles in the dissemination of information and innovation. The analysis of such networks attracted more attention in the research area. Social networks are modeled as graphs, called social graphs. An important property of social networks is that they have communities of entities with strong connections. Communities of social networks are groups of people sharing common interests or activities [1]. The typical way to identify communities is graph clustering

Results

Discussion

Conclusion