Using Interest Graphs to Predict Rich-Media Diffusion in Content-Based Online Social Networks

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Lakshmi S. Iyer, Associate Professor (Creator)
Xia Zhao, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site:

Abstract: Rich-media, pictures, and videos, are becoming an increasingly important aspect of online social networks. Unlike social networks, where users are connected primarily because of being friends, peers, or co-workers, content-based networks build connections between individuals founded on a shared interest in rich-media content. In this study, “interest-graphs” comprised of these content-based connections were examined. As shown, interest graph analysis provides important advantages over traditional social network analysis to identify valuable network members and predicting rich-media diffusion.

Additional Information

Information Systems Management
Language: English
Date: 2015
business intelligence, content-based network, interest graphs, social graphs, social network analysis, visual word of mouth

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