NTU talk: Community Discovery in Dynamic, Rich Media Social Networks

Title: Community Discovery in Dynamic, Rich Media Social Networks
Speaker: Yu-Ru Lin, PhD Candidate, Arizona State University.
Time: 3:50pm ~ 5:00pm, Wednesday, March 22, 2009.
Place: Room 111, CSIE building

Abstract: With the rapid proliferation of different types of social media, such as instant messaging (e.g., AIM, MSN, Skype), media sharing sites (e.g., Flickr, YouTube), blogs (e.g., Blogger, WordPress, LiveJournal), wikis (e.g., Wikipedia, PBWiki), microblogs (e.g., Twitter, Jaiku), social networks (e.g., MySpace, Facebook), to mention a few, users routinely produce (e.g. blogs) and consume media (e.g. YouTube) as well as interact with each other through a wide array of functionality provided by various social media. These social media depend largely on implicit communities of online users to deliver value. Identifying and analyzing the dynamics of such latent communities can lead to improved functionality of the social media as well as provide insight into the design of future online collaborative services. The problem is particularly important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, in expertise discovery, and guide long term enterprise reorganization.

In this talk, I will cover three aspects of community analysis in dynamic, rich media social networks: (1) Community evolution – How do we identify communities in large scale, dynamic social networks, and analyze their structures and evolutions? I will introduce a robust unified approach that discovers communities and captures their evolution with temporal smoothness given by historic community structure. (2) Community summarization – How do we summarize community activities, in order to trace community interests or retrieve community generated content? I will present a summarization framework that characterizes the time-evolving patterns of social activities with associated media objects in a community. (3) Multi-relational communities – How do we discover communities when the social networks exist in a highly connected web of contexts (e.g., social groups, geographic locations, time, etc.)? I will discuss a novel multi-relational non-negative tensor decomposition algorithm that aims to solve this problem. I will also show the effectiveness of these techniques in real world datasets collected from the blogosphere, an enterprise, Flickr, Digg, etc.

Short Biography: Yu-Ru Lin is currently a Ph.D. student in the School of Computing and Informatics at Arizona State University, with a concentration in Arts, Media and Engineering. Her advisor is Dr. Hari Sundaram. Her research interests include problems relating to dynamic multi-relational social network analysis – in particular, community dynamics, social information summarization and representation. Her research focuses on extracting human communities that collaborate around certain topics or media sharing activities. She has proposed non-negative matrix/tensor factorization techniques for analyzing community structures and evolutions in online social networks, as well as time-varying social relational data. Her work has been published in leading international conferences and journals. (Her publication can be found at http://www.public.asu.edu/~ylin56/pub.html.)
She has worked at NEC Labs America and IBM TJ Watson Research Center as a summer intern in 2006, 2007 and 2008. She has received awards including AME Student Excellence Award (2007 and 2008) and IBM PhD Fellowship Award (2009). She holds an M.S. and B.S. degree in Computer Science from National Chiao Tung University, Taiwan.