Day 1
16 July

0900    Opening Remarks

0915    Keynote
            William Corvey, DARPA
            Graphs and the Grounding Problem

1015    Break

1030    Session 1: Causal Inference and Experimentation

            Alexander Volfovsky, Duke University
            Neighborhood Adaptive Estimators for Causal Inference
            under Network Interference

            Simon Wein, University of Regensburg
            Graph Neural Networks for Causal Inference in Brain Networks

            Damon Centola, University of Pennsylvania
            The Science of Structural Intelligence

1200    Group Photo

1215    Lunch

1345    Session 2: Social Media Misinformation and Ethics

            David Rand, Massachusetts Institute of Technology
            Durably Reducing Conspiracy Beliefs through Dialogues with AI

            Jennifer Allen, NYU Stern School of Business
            Quantifying the Impact of Misinformation and
            Vaccine-Skeptical Content

            Juniper Lovato, University of Vermont
            Rethinking Consent in Socially-Networked Environments
            with Models of Distributed Consent

1515    Break

1530    Poster Session

1700    Break / Check-in

1730    Reception

1900    Banquet

Day 2
17 July

0845    Keynote

            Leman Akoglu, Carnegie Mellon University
            Graph Generation via Autoregressive Diffusion

0945    Break

1000    Session 3: Generative Graph Models

            Vagelis Papalexakis, University of California, Riverside
            Generating Low-Rank Graphs

            Bryan Perozzi, Google
            Giving a Voice to Your Graph:
            Representing Structured Data for LLMs

1100    Break

1115    Session 3: Generative Graph Models (continued)

            Benjamin Miller, MIT Lincoln Laboratory
            Complex Network Effects on the Robustness of
            Graph Convolutional Networks

            Wengong Jin, Broad Institute of MIT and Harvard
            Deep Generative Models for Drug Discovery

1215    Lunch

1330    Session 4: Emerging Applications

            Elenna Dugundji, Massachusetts Institute of Technology
            Predicting Traffic Intensity with Spatio-Temporal
            Graph Neural Networks: A Case Study during
            Road Network Maintenance

            Teddy Koker, MIT Lincoln Laboratory
            Higher-Order Equivariant Neural Networks for
            Charge Density Prediction in Materials

            Sutanay Choudhury, Pacific Northwest National Laboratory
            Scaling up Multi-Agentic Reasoning Systems

1500    Adjourn