Department of Computer Science and Technology at Tsinghua University
Talk title: Big Network Analysis—Algorithms, and Applications
Online social networks connect our physical daily life and the virtual Web space. The user generated data is becoming big, heterogeneous, and highly connected. In this talk, I will first present our recently developed methodologies and algorithms for connecting multiple heterogeneous networks (COSNET) and top-k similarity search (Panther). Both algorithms have been deployed to an online academic search and mining system AMiner, which has collected a large scholar dataset, with more than 130,000,000 researcher profiles and 100,000,000 papers from multiple publication databases. With COSNET, we connect AMiner with several professional social networks, such as LinkedIn and VideoLectures, which significantly enriches the scholar metadata. Panther is used to find similar authors in AMiner and can return top-k similar vertices 300× faster than the state-of-the-art methods.
Short Bio: Jie Tang is an associate professor with the Department of Computer Science and Technology at Tsinghua University, and was also visiting scholar at Cornell University. His interests include social network analysis, data mining, and machine learning. He has published more than 200 journal/conference papers and holds 20 patents. His papers have been cited by more than 6,000 times (Google Scholar). He served as PC Co-Chair of CIKM’16, WSDM’15, ASONAM’15, SocInfo’12, KDD-CUP/Poster/Workshop/Local/Publication Co-Chair of KDD’11-15, and Associate Editor-in-Chief of ACM TKDD, Editors of IEEE TKDE/TBD and ACM TIST. He leads the project AMiner.org for academic social network analysis and mining, which has attracted more than 8 million independent IP accesses from 220 countries/regions in the world. He was honored with the Newton Advanced Scholarship Award, CCF Young Scientist Award, and NSFC Excellent Young Scholar.
Computer Science Department at Stanford University
Talk title: Negative Social Influence in Online Discussions
Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. First, by studying four large comment-based news communities, we investigate the influence of such feedback on future user behavior. How may positive or negative feedback percolate through a community? Next, we focus on the impact of negative behavior (i.e., trolling) on a community - quantifying the effects of both trolls and trolling on the community at large through a combination of experimentation and large-scale longitudinal analysis.
Short Bio: Justin Cheng is a PhD candidate in the Computer Science Department at Stanford University, where he is advised by Jure Leskovec and Michael Bernstein. His research lies at the intersection of data mining and crowdsourcing, and focuses on cascading behavior in social networks. This work has received several best paper nominations at CHI, CSCW, and ICWSM. He is also a recipient of an MSR PhD Fellowship.
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