Role Analytics in Networks
Networks (or graphs) are widely used in representing complex relationships among entities, for instance, social networks, biological networks, and traffic networks. Roles capture the functions nodes play in a network. For example, nodes may function as the center, peripheral, clique or bridge. It is intuitive that two nodes play the same role in the network if they are structurally similar. Role analytics is an important task in graph mining and can shed light on a variety of applications in networks. Thus, it has been studied by social scientists, database researchers and data mining community. This tutorial aims to give a comprehensive discussion of role analytics in networks. We first briefly revisit the basic concepts of equivalence relations which lay the foundation for later research on role analytics. Then we categorize existing approaches for role discovery and analytics into different types according to the differences in techniques and overview some representative, distinct, and popular approaches for each category. Finally, we point out the challenges and future directions.
The goal of this tutorial is to offer a comprehensive presentation of role analytics in networks including basic concepts of structural roles, representative approaches from both sociology and computer science, and challenges and future directions. Role analytics is an exciting research topic studied by social scientists, database researchers and data mining community. This tutorial focuses on presenting state-of-the-art computational approaches for role analytics showing the connections across data mining, database and social science.
We will begin with the basic concepts of equivalence relations including structural, automorphic, regular, and stochastic equivalences, which lay the foundation for later research on role analytics. Then we categorize existing approaches for role analytics into equivalence-based, similarity-based, blockmodel-based, feature-based and embedding-based methods according to the differences in techniques and overview some representative, distinct, and popular approaches for each category. Finally, we will discuss the challenges and future directions.
Date: Monday, 14 September 2020
Time: 9:00 AM - 12:00 AM
Yulong Pei is a postdoc researcher with the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He received his Ph.D. in Computer Science from TU/e in February 2020. His research interests cover graph mining, network embedding, and text mining. He has over 30 publications including papers in top conferences, such as AAAI, CIKM, and COLING and IJCAI, and journals, such as DMKD and TKDE. He has served as the PC member of top-tier conferences including AAAI, IJCAI and ECMLPKDD, and the regular reviewer for prestigious journals like TKDE and TKDD.
George Fletcher is an associate professor with the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), where he chairs the Databases group. He studies data-intensive systems, with a focus on graph analytics. He recently co-organized GRADES-NDA 2019 workshop at SIGMOD and an NII Shonan Seminar on Graph database systems 2018. He also chaired the EDBT Summer School on Graph Data Management 2015. He serves on the major PC of data management and AI conferences.
Mykola Pechenizkiy is a full professor with the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), where he chairs the Data Mining group. In the past, he co-developed and delivered a series of tutorial on handling concept drift in machine learning, including tutorials at ECMLPKDD 2012 and ECMLPKDD 2010. It was later supported with the ACM CS survey paper. He co-authored over 100 papers in data mining and its applications including publications in DMKD, TKDE, KAIS, AAAI, IJCAI, SDM and ECMLPKDD among others. He is an active contributor to the ECMLPKDD community, serving as area chair in both Research and Applied Data Science Tracks, and serving on the editorial board of DMKD.