Symposium Overview
The symposium brings together leading experts from universities, industry, and government to explore the state of the art and define a future roadmap in network science. The themes of this year's symposium will be network influence and experimentation, knowledge-informed models, and graph generation and learning. In order to provide an interactive environment and promote strong interaction among the attendees, the event will be limited to a small group of invited attendees.
The symposium will take place on Tuesday, July 16th - Wednesday, July 17th, 2024 at the MIT Endicott House.
Symposium Highlights
As part of the two-day technical program this year, topics of interest will include:
- Social Dynamics and Influences
- Network Experimentation and Observational Studies
- Synthetic Graph Generation
- Network Similarity Metrics
- Physics-, Knowledge-Informed Models and Learning
- Explainable, Transparent Graph Learning
- Adversarial Graph Analysis and Learning
- Graph Learning for Combinatorial Optimization
- Recent Advances in Machine Learning on Graphs
- Various Applications Including Misinformation on Social Media, Cyber Defense,
Bio/Material Design, Climate Change, Resilient Infrastructures and Systems
Symposium Organizers
Chairs
Rajmonda Caceres | MIT Lincoln Laboratory
Dennis Ross | MIT Lincoln Laboratory
Sanjeev Mohindra | MIT Lincoln Laboratory
Technical Co-Chairs
Benjamin Miller | MIT Lincoln Laboratory
Edoardo Airoldi | Temple & Harvard University
Edward Kao | MIT Lincoln Laboratory
Lin Li | MIT Lincoln Laboratory
Technical Committee
Alexander Volfovsky | Duke University
Ali Pinar | Sandia National Laboratories
Christopher Long | U.S. Department of Defense
David Martinez | MIT Lincoln Laboratory
Heidi Perry | MIT Lincoln Laboratory
Johan Ugander | Stanford University
Jordan Crouser | Smith College
Robert Bond | MIT Lincoln Laboratory
Steven Smith | MIT Lincoln Laboratory