Social Topic Models for Community Extraction

Loading...
Thumbnail Image

View/Download File

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Social Topic Models for Community Extraction

Published Date

2008-02-11

Publisher

Type

Report

Abstract

With social interaction playing an increasingly important role in the online world, the capability to extract latent communities based on such interactions is becoming vital for a wide variety of applications. However, existing literature on community extraction has largely focused on methods based on the link structure of a given social network. Such link-based methods ignore the content of social interactions, which may be crucial for accurate and meaningful community extraction. In this paper, we present a Bayesian generative model for community extraction which naturally incorporates both the link and content information present in the social network. The model assumes that actors in a community communicate on topics of mutual interest, and the topics of communication, in turn, determine the communities. Further, the model naturally allows actors to belong to multiple communities. The model is instantiated in the context of an email network, and a Gibbs sampling algorithm is presented to do inference. Through extensive experiments and visualization on the Enron email corpus, we demonstrate that the model is able to extract well-connected and topically meaningful communities. Additionally, the model extracts relevant topics that can be mapped back to corresponding real-life events involving Enron.

Keywords

Description

Related to

Replaces

License

Series/Report Number

Technical Report; 08-005

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Other identifiers

Suggested citation

Pathak, Nishith; DeLong, Colin; Erickson, Kendrick; Banerjee, Arindam. (2008). Social Topic Models for Community Extraction. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215748.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.