Finding Communities in Site Web-Graphs and Citation Graphs

TL;DR

This paper introduces a fast algorithm for detecting communities in Web-graphs and citation graphs, which are social network representations, to improve search, ranking, and data-mining tasks.

Contribution

The paper presents a novel, efficient algorithm specifically designed for identifying communities in unweighted, undirected Web and citation graphs.

Findings

Algorithm effectively identifies communities in Web-graphs

Improves search and bibliographic ranking processes

Applicable to large unweighted, undirected graphs

Abstract

The Web is a typical example of a social network. One of the most intriguing features of the Web is its self-organization behavior, which is usually faced through the existence of communities. The discovery of the communities in a Web-graph can be used to improve the effectiveness of search engines, for purposes of prefetching, bibliographic citation ranking, spam detection, creation of road-maps and site graphs, etc. Correspondingly, a citation graph is also a social network which consists of communities. The identification of communities in citation graphs can enhance the bibliography search as well as the data-mining. In this paper we will present a fast algorithm which can identify the communities over a given unweighted/undirected graph. This graph may represent a Web-graph or a citation graph.