KEYNOTE SPEAKERS

Kathleen M. Carley has a H.D. from the University of Zurich in Business, Economics and Informatics; a Ph.D. from Harvard University in Sociology; and two S.B.’s from Massachusetts Institute of Technology – one in Political Science and on in Economics. She is a professor of Societal Computing in the Software and Social Systems Department in the School of Computer Science with secondary appointments in the Departments of Electrical and Computer Engineering, Engineering and Public Policy, and the Social and Decision Sciences and in the Heinz School of Public Policy at Carnegie Mellon University. She is the director of two university wide centers: the Center for Computational Analysis of Social and Organizational Systems (CASOS) that focuses on the application of network science and other data science techniques to the study of complex issues such as diffusion and terrorism, and the Center for Informed Democracy and Social-cybersecurity (IDeaS) that focuses on online harms such as disinformation, hate-speech, and extremism. She is also the CEO of Netanomics.

Dr. Carley’s research combines cognitive science, social networks and computer science to address complex social and organizational problems. Specific research areas are dynamic network analysis, computational social and organization theory, social adaptation and evolution, and social-cybersecurity. Carley and her students have developed infrastructure tools for analyzing large-scale dynamic networks and various agent-based simulation systems. The infrastructure tools include: ORA, a statistical toolkit for analyzing and visualizing high dimensional networks in general and through time and space. NetMapper - a text-mining system for extracting semantic networks from texts, sentiment, cognitive cues, and other content. Her simulation models include: Construct – which is a general model for information and belief diffusion within and across media, and OSIRIS – which is a simplified digital twin of an organization undergoing various types of cyber attacks.

Dr. Carley is an IEEE fellow. She has served on numerous national academies panels. In addition she has received multiple awards including the Simmel award from INSNA, the United States Geospatial Intelligence Foundation Academic Award.

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From an ecological perspective people and their networks adapt and link to other networks all of which co-evolve form dynamic high-dimensional networks. High dimensional networks are ones with one or more types of nodes (multi-modal) and one or more types of links (multi-plex) where there are a set of networks linked together by sharing at least one node. People are constrained and enabled by their position in these high dimensional systems, and are impacted by the whole not just by one network at a time. For example, in organizations people are embedded in both a social network of who knows who, a knowledge network of who knows what, an assignment network of who is doing what, a task requirements network of what knowledge is needed to do which task, and so forth. Today, high dimensional networks are easier to collect due to digital data and novel computational techniques for extracting networks from texts. Importantly, all these networks are coupled. As is classically the case for complex systems, a little coupling is valuable and a very high degree can lead to problems if not catastrophes. In this talk the power of high dimensional networks for addressing social issues and considerations that need to be addressed in new algorithms are discussed. It is argued that high dimensional networks often provide more explanatory power and enable prediction in cases where a social network alone does not, and that the co-evolution of the networks must be tracked to assess impact. New techniques that take such high dimensionality into account are described.

Danai Koutra is an Associate Professor in Computer Science and Engineering at the University of Michigan, where she leads the Graph Exploration and Mining at Scale (GEMS) Lab. She is also an Amazon Scholar. Her research focuses on principled, practical, and scalable methods for large-scale real networks, and her interests include graph learning, graph neural networks, graph summarization, knowledge graph mining, graph learning, similarity and alignment, and anomaly detection. She has won an NSF CAREER award, an ARO Young Investigator award, the 2020 SIGKDD Rising Star Award, research faculty awards from Google, Amazon, Facebook and Adobe, a Precision Health Investigator award, the 2016 ACM SIGKDD Dissertation award, and an honorable mention for the SCS Doctoral Dissertation Award (CMU). She holds a patent on bipartite graph alignment, and has multiple papers in top data mining conferences, including 9 award-winning papers and the 2022 IEEE ICDM Test-of-Time Award. She is Program co-Chair for ECML/PKDD 2023 and an Associate Editor of ACM TKDD. She was a track co-chair for The Web Conference 2022, a co-chair of the Deep Learning Day at KDD 2022, the Secretary of the new SIAG on Data Science in 2021, and has also routinely served in the organizing committees of all the major data mining conferences. She has worked at IBM, Microsoft Research, and Technicolor Research. She earned her Ph.D. and M.S. in Computer Science from CMU, and her diploma in Electrical and Computer Engineering at the National Technical University of Athens.

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Graph learning, which leverages the complex relationships between entities in non-Euclidean data, has a wide range of high-impact applications, including recommendation systems, bioinformatics, fraud detection, and more. Graph neural networks (GNNs) have become one of the most popular graph deep learning models, achieving state-of-the-art results for semi-supervised classification, in which the goal is to infer the unknown labels of the nodes given partially labeled networks with node features. While many different GNN models have been proposed, most of them perform best in graphs that exhibit the property of homophily, sometimes referred to as the idea that “birds of a feather flock together”, in which linked nodes often belong to the same class or have similar features. However, in the real world, there are also many settings where “opposites attract”, leading to networks that exhibit heterophily, in which linked nodes tend to be from different classes. Many of the most popular GNNs fail to generalize well to networks with heterophily (low homophily). In this talk, I will present my group’s work on identifying effective GNN designs and introducing new architectures that can improve performance in heterophilous networks. I will also discuss connections between heterophily and other well-known challenges of GNNs, including oversmoothing, robustness, performance discrepancies across nodes, and scalability.

Tao Zhou is the founding director of the Big Data Research Center at the University of Electronic Science and Technology of China. His main research interests include network science (e.g., link prediction, influential node identification, epidemic spreading, etc.) and computational socioeconomics. He has published many research articles in prestigious journals (e.g., Physics Reports, PNAS, Nature Communication, PRL, etc.), which received >34000 citations from Google Scholar, with H-index=86. His works have been reported by many academic medias as Nature News, PNAS News, MIT Technology Review, Sci. Am., PhysOrg.com, My Science, TG Daily, Dutch Science Magazine, Chinese Science News, etc. 

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Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of non-observed links, based on known topology. This talk will briefly introduce three recent debates on link prediction. (1) Do machine learning models, including ensemble learning techniques, performs better than mechanism algorithms? (2) Which metric(s) can best evaluate algorithm performance: AUC, AUPR, Precision or others? (3) Are the shorter paths more important in link prediction than longer paths? For example, do 2-hop-based indices perform better than 3-hop-based indices?