Mining community structure of named entities from free text

Abstract

Although community discovery based on social network has been studied extensively in the Web hyperlink environment, limited research has been done in the case of Web documents. The co-occurrence of Words and entities in sentences and documents usually implies some connections among them. Studying such connections may reveal important relationships. In this paper, we investigate the cooccurrences of named entities in Web pages and blogs, and mine communities among those entities. We show that identifying communities in such an environment can be transformed into a graph clustering problem. A hierarchical clustering algorithm is then proposed, which exploits triangle structures within the graph and the mutual information between vertices. Our empirical study shows that the proposed algorithm is promising in discovering communities from Web documents.

WisColl: Collective wisdom based blog clustering

“…Such is the case for [28], which uses a hierarchical structure for linkage based clustering measured by similarities of other objects linked to a pair of objects, where objects can refer to authors, papers, links, and web sites. Similar work is accomplished by Xin Li and Liu [25], where the authors use hierarchical clustering to identify communities by establishing connections per the cooccurrence of words in entities such as web pages and blogs. Authors in [8,12,23] present reviews of clustering algorithms for a collection of documents.…”

A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network

“…It groups nodes into a cluster if the nodes are similar and then successively merges clusters until all nodes have been merged into a single remaining cluster. Techniques based on hierarchical clustering have been used to quantify the structure of community in documents [32], web pages, blogs, [33] and discussion groups [34]. Hierarchical clustering using such algorithms as in , results in a hierarchy (tree) being formed where the leaves of the tree are the nodes that are clustered.…”

Tracking cohesive subgroups over time in inferred social networks

“…Hierarchical clustering is often used to quantify the structure of community in web networks (e.g. Girvan and Newman 2002, Donetti and Munoz 2004, Clauset 2005, Li et al 2006 where the cluster orderings in the dendrogram form the subgroups. Hierarchical clustering can automate the process of finding subgroups.…”