Inside the Atoms: Mining a Network of Networks and Beyond

Dartmouth Events

Inside the Atoms: Mining a Network of Networks and Beyond

Networks appear in many high-impact applications, often collected from different sources/times.. In this talk, I will present our recent work on mining such multiple networks.

Monday, March 5, 2018
Kemeny Hall 007
Intended Audience(s): Public
Categories: Lectures & Seminars

Abstract: Networks appear in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining models, including a network of networks model, a network of co-evolving time series model, a network of regression models, etc. Second, we will present some algorithmic examples on how to perform mining with such new models where the key idea is to leverage the contextual network as an effective regularizer during the mining process, including ranking, imputation, prediction and inference. Finally, we will demonstrate the effectiveness of our new models and algorithms in some applications, including bioinformatics, sensor networks, critical infrastructure networks and scholarly data mining. I will also introduce some other recent work on mining network-related data and share thoughts about the future plan.

Bio: Hanghang Tong is currently an assistant professor at School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. Before that, he was an assistant professor at Computer Science Department, City College, City University of New York, a research staff member at IBM T.J. Watson Research Center and a Post-doctoral fellow at Carnegie Mellon University. He received his M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2008 and 2009, both majored in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper Award (2015), four best paper awards (TUP'14, CIKM'12, SDM'08, ICDM'06), five `bests of conference' (KDD'16, SDM'15, ICDM'15, SDM'11 and ICDM'10), 1 best demo, honorable mention (SIGMOD'17), and 1 best demo candidate, second place (CIKM'17). He has published over 100 referred articles. He is an associated editor of SIGKDD Explorations (ACM), an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Neurocomputing Journal (Elsevier); and has served as a program committee member in multiple data mining, databases and artificial intelligence venues (e.g., SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc.).

For more information, contact:
Sandra Hall

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