At least one author of each accepted paper must be registered by the author registration deadline in order for that contribution to appear in the proceedings and to be scheduled for presentation.

Registration deadline: October 17, 2025

Extra abstract registration rate is applicable only once for each full registration.
Authors with 3 accepted submissions must register twice
1 registration for one submission and one extra submission
1 registration for the third submission

Extra abstract registration rate is applicable only once for each full registration.
Authors with 3 accepted submissions must register two times
1 registration for one submission and one extra submission
1 registration for the third submission

Extra Page Fee (max 2 pages over the 12 pages limit)

Other Author of a contribution or attendee with no contribution.

This student fee is for non-author attendees. It is not available to students if you are an author of papers or an abstract.

Despite its inevitable nature and the incontrovertible wisdom that “failure is the mother of success”, our quantitative understanding of failure (and how it leads to success) remains limited, in part due to the lack of systematic data and methods that record the frequently occurring yet often neglected failures within individuals, teams and organizations. This situation is changing radically, however, thanks to newly available large-scale datasets spanning social, scientific, and technical domains. In this tutorial, I will explore diverse examples of failure through the lens of data analytics, mathematical modeling, and causal inference. Drawing on computational tools from complexity and data sciences, I will show how a data-driven approach can illuminate the mechanisms behind the why, how, and when of failure. Beyond deepening our theoretical understanding of complex social systems, advances in this area have important policy implications and the potential to improve predictions, interventions, and designs that turn failure into a stepping stone for success.

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.

The dinner banquet is included in the author and other attendees registration. This fee is for an extra guest.