Spatial networks: New results and Challenges
Marc Barthelemy is a former student of the Ecole Normale Superieure of Paris. In 1992, he graduated at the University of Paris VI with a thesis in theoretical physics titled "Random walks in random media". After his thesis, he focused on disordered systems and their properties, and since 1992, he has held a permanent position at the CEA. Marc Barthelemy is now research director at the Institute of Theoretical Physics (IPhT) in Saclay and a member of the Center of Social Analysis and Mathematics (CAMS) at the Ecole des Hautes Etudes en Sciences Sociales (EHESS). His research interests moved towards applications of statistical physics to complex systems, complex networks, theoretical epidemiology, and spatial networks. Focusing on both data analysis and modeling with the tools of statistical physics, Marc Barthelemy is also working on various aspects of the emerging science of cities.
Queen Mary University of London, UK
Higher-order networks and their dynamics
Ginestra Bianconi is Professor of Applied Mathematics in the School of Mathematical Sciences of Queen Mary University of London and she is Alan Turing Fellow at the Alan Turing Institute. Currently she is Chief Editor of JPhys Complexity, Editor of PloSOne, and Scientific Reports, and she is Associate Editor of Chaos, Solitons and Fractals. In 2020 she was awarded the Network Science Fellowships by the NetSci Society. Her research activity on Statistical Mechanics and Network Science includes Network Theory and its interdisciplinary applications. She has formulated the Bianconi-Barabasi model that displays the Bose-Einstein condensation in complex networks. She has worked in network entropy and network ensembles and on dynamical processes on networks. In the last years, she has been focusing on multilayer networks, simplicial complexes, network geometry and topology, percolation and network control. She is the author of the book Multilayer Networks: Structure and Function by Oxford University Press.
University of Porto, Portugal
Mining Evolving Large-Scale Networks
João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurIA Fellow, IEEE Fellow, and member of the board of directors of the LIAAD, a group belonging to INESC Porto. His h-index at Google Scholar is 58. He is an Editor of several top-level Machine Learning and Data Mining journals. He has been ACM Distinguish Speaker. He served as Program Chair of ECMLPKDD 2005, DS09, ADMA09, EPIA 2017, DSAA 2017, served as Conference Chair of IDA 2011, ECMLPKDD 2015, DSAA’2021, and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. His main research interests are in knowledge discovery from data streams, evolving data, probabilistic reasoning, and causality. He published more than 300 reviewed papers in journals and major conferences. He has an extensive list of publications in data stream learning.
ETH Zürich, Switzerland
How Networks Can Change Everything for Better or for Worse
Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and affiliate of the Computer Science Department at ETH Zurich. In January 2014 Prof. Helbing received an honorary PhD from Delft University of Technology (TU Delft). Since June 2015 he is affiliate professor at the faculty of Technology, Policy and Management at TU Delft, where he leads the PhD school in "Engineering Social Technologies for a Responsible Digital Future". Dirk Helbing started as a physicist. With his diploma thesis, he initiated the area of pedestrian, crowd, and evacuation modeling and simulation. During his PhD and habilitation in physics, he helped to establish the fields of socio-, econo- and traffic physics. He was also co-founder of the Physics of Socio-Economic Systems Division of the German Physical Society (DPG). The work of Prof. Helbing is documented by hundreds of media reports and publications, among them more than 10 papers in Nature, Science, and PNAS. He won various prizes, including the Idee Suisse Award. He co-founded the Competence Center for Coping with Crises in Complex Socio-Economic Systems, the Risk Center, the Institute for Science, Technology and Policy (ISTP) and the Decision Science Laboratory (DeSciL). While coordinating the FuturICT initiative (www.futurict.eu), he helped to establish data science and computational social science in Europe, as well as global systems science.
Complexity science has been studying the result of dynamical and network interactions in multi-agent systems for a long time. This work has revealed many intriguing relationships between structure, function, and dynamics. By now, it is known that the interaction mechanisms and the network structure can decide about success or failure, disease or disaster. I will present examples from logistics to economics, from game theory to society, and from traffic light control to pandemics to illustrate how changing the network structure and interactions can make all the difference between paradies and hell.
Graph ODEs for Dynamic System Learning
Yizhou Sun is an associate professor at department of computer science of UCLA. 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/networks. Yizhou has over 100 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF CAREER Award, 2016 CS@ILLINOIS Distinguished Educator Award, Amazon Research Awards, and Okawa Foundation Research Award.
Northeastern University, USA
Prof. Vespignani received his undergraduate degree and Ph.D., both in physics and both from the University of Rome “La Sapienza,” in 1990 and 1994 respectively. He completed his postdoctoral research at Yale University and Leiden University. Prof. Vespignani worked at the International Center for Theoretical Physics (UNESCO) in Trieste and at the University of Paris-Sud in France as a member of the National Council for Scientific Research (CNRS) before moving to Indiana University in 2004. Before joining Northeastern University Vespignani was J.H.Rudy Professor of Informatics and Computing at Indiana University and serving as the Director of the Center for Complex Networks and Systems Research and the Associate Director of the Pervasive Technology Institute. Vespignani is elected fellow of the American Physical Society, member of the Academy of Europe, and fellow of the Institute for Quantitative Social Sciences at Harvard University. He has received the Honorary Doctorate from the Technical University of Delft, the Netherlands, and the 2016 Aspen institute Italia award. He is serving in the board/leadership of a variety of professional association and journals and the Institute for Scientific Interchange Foundation.
Vespignani has worked in a number of areas of non-equilibrium particle systems, statistical physics and computational sciences, including characterization of non-equilibrium phase transitions, fractal growth and self-organized criticality. Recently Vespignani’s research activity focuses on the interdisciplinary application of statistical and numerical simulation methods in the analysis of epidemic and spreading phenomena and the study of biological, social and technological networks. For several years he has been working on the characterization and modeling of the Internet, the WWW and large-scale information networks. He is now focusing his research activity in modeling the spatial spread of epidemics, including the realistic and data-driven computational modeling of emerging infectious diseases, the resilience of complex networks and the behavior of techno-social systems.
Vespignani has published 180+ peer reviewed papers in top rated scientific journals, including Nature, Science and PNAS that have accrued more than 50,000 citations according to the Google Scholar database. He is author, together with Romualdo Pastor-Satorras, of the book Evolution and Structure of the Internet. Together with Alain Barrat and Marc Barthelemy he has published in 2008 the monograph Dynamical Processes on Complex Networks. In 2019 he has published for Springer “Charting the Next Pandemic”, with A.Pastore y Piontti, N.Perra, L.Rossi & N. Samay.