This tutorial offers a guided tour of the open, evolving textbook Network Science Data & Models, a Jupyter Book that accompanies the Python-based network analysis course at Northeastern University. We begin with a concise walkthrough of the early chapters—data ingestion, descriptive measures, community detection, generative models, and network dynamics—highlighting the notebooks already included in the textbook. The second half is a focused, hands-on exploration of spatial networks. Starting with random geometric graphs as baselines, we transform real-world shapefiles into planar graphs using GeoPandas, Shapely, and OSMnx. We then enrich these graphs with population-level data and show several statistical approaches for analyzing spatial data. Participants will leave with reproducible notebooks, curated datasets, and clear guidance on submitting pull requests to contribute their own chapters to this book in the future. The goal is not only to discuss spatial network analysis but also to empower the community to extend this shared, open textbook in support of both research and teaching.

Biography

Brennan Klein is core faculty at the Network Science Institute at Northeastern University and Assistant Teaching Professor in the Department of Physics. He is the director of the Complexity & Society Lab, which spans two broad research areas: 1) Information, emergence, and inference in complex systems — developing tools and theory for characterizing dynamics, structure, and scale in networks, and 2) Public health and public safety — creating and analyzing large-scale datasets that reveal inequalities in the U.S., from epidemics to mass incarceration. In 2023, Prof. Klein was awarded the René Thom Young Researcher Award, given to a researcher to recognize substantial early career contributions and leadership in research in Complex Systems-related fields. He received a PhD in Network Science in 2020 from Northeastern University and a BA in Cognitive Science from Swarthmore College in 2014.

Human social interactions or contacts usually occur at specific times rather than continuously. They can be represented as temporal networks or as temporal hypergraphs when an interaction involves a group of individuals. Such networks facilitate the spread of information, opinions and epidemics. In this talk, we study these spreading processes on temporal networks and temporal hypergraphs. We address problems regarding influence estimation, spreading trajectories, and network robustness with the objective of identifying the relation between a node’s or link’s properties in the network and its role in the dynamic process. Specifically, how can we utilize the partially observed temporal network around each node to estimate the ranking of nodes by spreading influence? The average number of nodes infected over a given period starting from a single seed node is called the influence of that node. Which types of hyperlinks contribute more to a spreading process, i.e., lead to more nodes being infected directly via their activation? Which types of links, when removed, most strongly degrade the efficiency of information transport? To this end, we introduce theoretically grounded centrality metrics specially designed for addressing these problems.

Biography

Dr. Huijuan Wang is an Associate Professor in the department of Intelligent Systems at Delft University of Technology. Her research focuses on network data science. She develops methodologies to model, control and predict dynamic processes on time-evolving complex networks. Her work addresses diverse applications, ranging from epidemic spreading and opinion interactions to social and financial contagion, resilience of infrastructures and the organisation of criminal networks. Dr. Wang was a visiting scientist in the Department of Physics at Boston University (2011-2019), as well as in the Departments of Electrical Engineering at Stanford (2015) and Princeton (2022) Universities. She is the Co-founder of the Dutch Network Science Society. She has served as Chair of the Netherlands Platform for Complex Systems and as a board member of the Network Science Society.