Spatial Networks of Knowledge

Luca Maria Aiello - Nokia Bells Lab Cambridge, UK

Abstract
While great emphasis has been placed on the role of social interactions as driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among US metropolitan areas, and found that node centrality computed on this network accounts for most of the variability observed in cities' innovation performance. We attempted to generalize this paradigm to social media data by applying advanced NLP tools to online conversations with the aim of inferring the network of knowledge exchange between US states. We found that economic growth of a region is predicted much more accurately by the small fraction of social ties characterized by knowledge and trust than considering all ties indiscriminately. Our finding provides a first example of how open data and NLP could open up a novel way to model networks of exchange of non-material resources (such as knowledge) and to produce more nuanced, interpretable, and predictive network models of societal growth.
Bio

Luca Maria Aiello is a Senior Research Scientist at Nokia Bell Labs Cambridge, UK. Formerly, he has been a Research Scientist at Yahoo Labs for 5 years and a research Fellow of the ISI Foundation in Torino, Italy. He conducts interdisciplinary research in computational social science, network science, and urban informatics. His work has been covered by hundreds of news articles published by news outlets worldwide including Wired, WSJ, and BBC.

Modelling Complexity Across Scales to Achieve Sustainable Urban Systems

Roger Cremades - Wageningen University & Research, the Netherlands

Abstract
Understanding how to create social tipping in urban systems, and the magnitude of change needed to make a difference in sustainability with implications on the regional components of the Earth system requires understanding of cross-scale phenomena, from citizen to neighbourhood and metropolitan area, from city to river basin, and from city to region. Complex systems science is a fundamental ingredient to model cross-scale phenomena. We will explore how to cross scales and showcase ongoing applications from economic experiments for consumers, producers and intermediate suppliers aiming to provide detailed understanding on how to trigger social tipping and change towards sustainability, and how to use experimental insights on models based on agents, stocks and flows, and networks at the neighbourhood and metropolitan scales. Then we will connect the metropolitan scale to growing scarcities under climate change and covid19 at river basin, region and global levels. Beyond systems understanding, our goal is to provide key input to the policy community, and a few examples of how (not) to communicate complexity to policy- and decision-makers will be shown and discussed.
Bio

Dr. Roger Cremades is a complex systems scientist and heterodox global change economist integrating human-Earth interactions across systems and scales into modular quantitative tools, e.g. connecting drought risks in cities with land use at the river basin scale. He is co-Chair of the Development Team of the Finance and Economics Knowledge-Action Network of Future Earth (2020-2022), the largest global research programme in global change. Roger coordinated research and co-production projects above €1M, and published in top journal like PNAS, Nature Climate Change, and Nature Geoscience.

DataDiVR - A Virtual Reality platform for exploring complex networks

Jörg Menche - Max Perutz Labs, University of Vienna, Austria

Abstract
Networks provide a powerful representation of complex systems of interacting components. In addition to a wide range of available analytical and computational tools, networks also offer a visual interface for exploring large data in a uniquely intuitive fashion. However, the size and complexity of many networks render static visualizations on common screen or paper sizes impractical and result in proverbial 'hairballs'. Here, we introduce an immersive Virtual Reality (VR) platform that overcomes these limitations and unlocks the full potential of visual, interactive exploration of large networks. Our platform is designed towards maximal customization and extendibility, with key features including import of custom code for data analysis, easy integration of external databases, and design of arbitrary user interface elements. Our platform represents a first-of-its-kind, general purpose VR data exploration platform in which human intuition can work seamlessly together with state-of-the-art analysis methods for large and diverse data.
Bio

Jörg Menche studied physics in Germany and Brazil and obtained a PhD at the Max Planck Institute for Colloids and Interfaces in 2010. He worked as a postdoc at Northeastern University and Harvard Medical School in Boston, before starting his own research group at the CeMM Research Center for Molecular Medicine in Vienna in 2015. In 2020 he became full professor at the University of Vienna where he holds a shared appointment at the Center for Molecular Biology (Max Perutz Labs) and the Faculty of Mathematics. His interdisciplinary team combines backgrounds ranging from biology and bioinformatics to medicine, physics, mathematics & arts. The broad ambition of his group is to use tools and concepts from network theory to elucidate the complex machinery of interacting molecules that constitutes the basis of (patho-)physiological states. Major areas of interest are network-based approaches to rare diseases, understanding the basic principles of how perturbations of biological systems influence each other and developing novel Virtual Reality (VR) based technologies for analyzing large genomic data.

Spatially-resolved modeling and control of COVID-19 spreading

Alessandro Rizzo - Politecnico di Torino, Italy

Abstract
To date, the only effective means to respond to the spreading of COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reduce their social and economical consequences Here, we introduce two different modeling paradigms able to model the spreading of COVID-19, explicitly accounting for spatial resolution at different scales. The introduced models are valuable forecast tools, as well as support systems to perform what-if analyses and inform the application of containment policies in urban environments (or wider ones).
First, we introduce a meta-population spatially-resolved model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and apt to evaluate the outcomes of these two types of NPIs. Our approach combines the advantages of granular spatial modeling of meta-population models with the ability to realistically describe social contacts via activity-driven networks. We provide a valuable framework to assess the viability of different NPIs, varying with respect to their timing and severity, at different spatial scales, from city neighborhoods to entire countries. Furthermore, an agent-based modeling platform to simulate the spreading of COVID-19 at the resolution of a single individual in small towns and cities is proposed and demonstrated on real data from New Rochelle, NY —where the first outbreak was registered in the United States. The model explicitly considers disease transmission in residential buildings and different public locations within a statistically realistic population, and accounts for different types of testing. Results suggest that the effects of mobility restrictions largely depend on the possibility to implement timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritized afterwards.
Bio

Alessandro Rizzo received the Laurea degree (summa cum laude) in computer engineering and the Ph.D. degree in automation and electronics engineering from the University of Catania, Italy, in 1996 and 2000, respectively. In 1998, he worked as a EURATOM Research Fellow with JET Joint Undertaking, Abingdon, U.K., researching on sensor validation and fault diagnosis for nuclear fusion experiments. In 2000 and 2001, he worked as a Research Consultant at ST Microelectronics, Catania Site, Italy, and as an Industry Professor of robotics with the University of Messina, Italy. From 2002 to 2015, he was a tenured Assistant Professor with the Politecnico di Bari, Italy. In November 2015, he joined the Politecnico di Torino. Since 2012, he has been a Visiting Professor with the New York University Tandon School of Engineering, Brooklyn, NY, USA. He is currently an Associate Professor with the Department of Electronics and Telecommunications, Politecnico di Torino, Italy. He is engaged in conducting and supervising research on complex networks and systems, modeling and control of nonlinear systems, and cooperative robotics. He is the author of two books, two international patents, and more than 150 papers on international journals and conference proceedings. He has been a recipient of the Award for the Best Application Paper at the IFAC world triennial conference in 2002 and of the Award for the Most Read Papers in Mathematics and Computers in Simulation (Elsevier) in 2009. He is also a Distinguished Lecturer of the IEEE Nuclear and Plasma Science Society and one of the recipients of the 2019 Amazon Research Awards.