Finding Communities in Site Web-Graphs and Citation Graphs
This work addresses the need for community detection to improve search engines, prefetching, and data-mining, but appears incremental as it builds on existing graph-based methods without claiming major breakthroughs.
The authors tackled the problem of identifying communities in web-graphs and citation graphs, presenting a fast algorithm that can detect these communities in unweighted/undirected graphs, though no concrete performance numbers are provided.
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.