Exploring the Connections Between Social Network Analysis and Graph Analytics
Abstract
Social network analysis and graph analytics are closely related fields, yet they have developed with different emphases—interpretability and domain insight on one side, scalability and algorithmic generality on the other. In this keynote, I explore the connections between the two, examining how graph analytic techniques can support the study of social structures, and how problems from social network analysis can inspire new challenges for graph analytics research. Drawing on both foundational concepts and real-world examples, the talk reflects on opportunities for mutual enrichment and outlines possible directions for deeper integration.
Short Bio
M. Tamer Özsu is a University Professor in the David R. Cheriton School of Computer Science at the University of Waterloo and Co-Director of the Faculty of Mathematics graduate program on Data Science and AI. He served as the Director of the Cheriton School from January 2007 to June 2010 and as the Associate Dean of Research for the Faculty of Mathematics from January 2014 to June 2016. His PhD degree is from the Ohio State University (1983). He is a Fellow of the Royal Society of Canada, American Association for the Advancement of Science (AAAS), Science Academy, Türkiye, Asia-Pacific Artificial Intelligence Association (AAIA), and the Balsillie School of International Affairs (BSIA); he is also Life Fellow of Association for Computing Machinery (ACM) and The Institute of Electrical and Electronics Engineers (IEEE). He is the recipient of the University of Waterloo Excellence in Graduate Supervision Award (2025), ACM Presidential Award (2024), IEEE Technical Committee on Data Engineering (TCDE) Education Award (2024), IEEE Innovation in Societal Infrastructure Award (2022), CS-Can/Info-Can Lifetime Achievement Award (2018), ACM SIGMOD Test-of-Time Award (2015), the ACM SIGMOD Contributions Award (2006), The Ohio State University College of Engineering Distinguished Alumnus Award (2008), and multiple Outstanding Performance Awards at the University of Waterloo His publications have received four best paper awards and one honorable mention.Towards Ordinal Data Science
Abstract
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of methods for ordinal data is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this talk, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures and show how to infer knowledge from them. We will illustrate this approach by presenting first results for two ways of measuring structural properties in Formal Concept Analysis (FCA), a theory for deriving concept hierarchies from datasets: First, we introduce the `relative dimension' for ordered sets - a new measure for the dimension of an ordered set that is inpired by the Hausdorff dimension for fractals in metric spaces. In a second line, we argue that the distributivity law in (concept) lattices is beneficial for data analyis, and study to which extent concept lattices derived from real-world data follow this law. We also discuss first steps for 'repairing' non-distributivity.
Short Bio
Gerd Stumme is Full Professor of Computer Science at the University of Kassel, Germany. He is leading the Chair on Knowledge and Data Engineering and is executive director of the Research Center for Information Systems Design (ITeG), director of the International Centre for Higher Education Research (INCHER), and founding member of the Hessian Institute for Artificial Intelligence (hessian.AI). Gerd Stumme is researching and developing methods and algorithms at the confluence of Data Science, Artificial Intelligence and Mathematics. He obtained his PhD in 1997 under the guidance of Rudolf Wille, the founder of Formal Concept Analysis, and has 30 years of research experience in this field. He is interested in the development of methods for the semantic and structural analysis of (social) networks and for the discovery of concept hierarchies. In the last years, Gerd Stumme has worked in the research areas Semantic Web, Web Mining, Social Bookmarking systems, and recommender systems. Recently, he is returning to his mathematical roots, studying how to exploit mathematical structures (in particular graphs and ordered sets) for knowledge acquisition and knowledge communication.Seeing the Unseen: Graph Representation Learning for Information Network Analysis
Abstract
Graph representation learning has become a cornerstone of modern information network analysis by transforming discrete topological structures into continuous vector embeddings, enabling advanced modeling and prediction on complex networks. In this talk, we will explore its broad applications, including (1) understanding social network formation, (2) detecting Twitter user ideology, (3) identifying key influencers in complex network, and (4) mining social dynamics. Despite its success, graph representation learning, particularly through graph neural networks (GNNs), is often treated as a black-box methodology, raising concerns about interpretability and fairness. We will discuss recent advances addressing these challenges to enable trustworthy analysis and deployment. Finally, we will briefly discuss the role of generative AI techniques in social network analysis.
Short Bio
Yizhou Sun is a professor at department of computer science of UCLA and an Amazon Scholar. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/networks, and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network, with a recent focus on deep learning on graphs, AI for Chip Design, AI for Science, and neuro-symbolic reasoning. Yizhou has over 180 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She is a recipient of multiple Best Paper Awards, ACM SIGKDD Doctoral Dissertation Award, Yahoo ACE (Academic Career Enhancement) Award, NSF CAREER Award, CS@ILLINOIS Distinguished Educator Award, Amazon Research Awards (twice), Okawa Foundation Research Award, VLDB Test of Time Award, WSDM Test of Time Award, ACM Distinguished Member, IEEE AI’s 10 to Watch, and SDM/IBM faculty award. She is a general co-chair of SIGKDD 2023, PC co-chair of ICLR 2024, and PC co-chair of SIGKDD 2025.
