SDM2020 Tutorial -- Multi-modal Network Representation Learning: Methods and Applications

Description

In today’s information and computational society, complex systems are often referred as multi-modal networks with heterogeneous structure or heterogeneous content or both. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for customized intelligent solutions in respond to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.
Keywords: Multi-modal network, Network representation learning, Deep learning

Presenters

Chuxu Zhang is a Ph.D. Candidate in the Department of Computer Science and Engineering at the University of Notre Dame. His research interests are data science, machine learning, artificial intelligence, and their applications in graphs/networks mining, recommendation/personalization, natural language processing, time series/spatial-temporal data analysis, synthetic chemistry, etc. His works have appeared in premier data science and artificial intelligence conferences including KDD, WWW, AAAI, IJCAI, etc. He has served as the conference PC member in ICLR, KDD, AAAI, IJCAI, etc., and the journal reviewer for TKDE, TNNLS, etc.

Meng Jiang is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. His research interests include data mining, machine learning, and information extraction. His research work focuses on computational behavior modeling. He has published over 50 conference and journal papers of the topics. His work was recognized as ACM SIGKDD 2014 Best Paper Finalist. He has delivered six tutorials in conferences such as KDD, SIGMOD, WWW, CIKM, and ICDM. He is the recipient of Notre Dame Global Gateway Faculty Award.

Xiangliang Zhang is an Associate Professor of Computer Science and directs the Machine Intelligence and Knowledge Engineering (http://mine.kaust.edu.sa) group at KAUST, Saudi Arabia. Dr. Zhang’s research mainly focuses on learning from complex and large-scale streaming and graph data. Dr. Zhang has published over 100 research papers in referred international journals and conference proceedings, including TKDE, SIGKDD, AAAI, IJCAI, ICDM, VLDB J, ICDE etc. Dr. Zhang is selected and invited to deliver an Early Career Spotlight talk at IJCAI-ECAI 2018.

Nitesh V. Chawla is the Frank M. Freimann Professor in the Department of Computer Science and Engineering at the University of Notre Dame and the Director of the Center for Network and Data Science (CNDS) at Notre Dame. His research focuses on machine learning, AI and network science fundamentals and interdisciplinary applications. He is passionate on interdisciplinary collaborations to address the grand challenge problems for societal impact. His papers have received several best paper nominations and awards. He is also the recipient of several awards and honors including IEEE CIS Outstanding Early Career Award, the IBM Watson Faculty Award, the IBM Big Data and Analytics Faculty Award, the National Academy of Engineering New Faculty Fellowship, and 1st Source Bank Technology Commercialization Award. In recognition of the societal and impact of his research, he was recognized with the Rodney Ganey Award and Michiana 40 Under 40. He is the director of Interdisciplinary Center for Network Science and Applications (iCeNSA) and founder of Aunalytics, a data science software and solutions company.

Materials

[Two Page Notes]

[Slides]