Graphs Across AI: From Structural Reasoning to Foundation Models

Abstract

Graphs provide a universal language for representing relationships, dependencies, and structural patterns across diverse domains. Their flexibility enables them to bridge data, models, and knowledge, establishing a connection between symbolic reasoning, statistical learning, and neural computation. While Graph Neural Networks (GNNs) have popularized graph-based learning, the role of graphs in Artificial Intelligence (AI) extends far beyond this paradigm. Graphs underpin computational structures in neural architectures, capture relational dependencies in attention mechanisms, organize knowledge through Knowledge Graphs (KGs), and model interactions among agents in social, multimodal, and dynamic environments.

This workshop aims to explore the expanding role of graph-based reasoning and representation across AI, moving beyond GNNs toward structure-aware learning both for and with foundation models. It seeks to foster discussion among researchers investigating how graphs can unify reasoning, learning, and computation across scales and modalities. 

The goal is to consolidate theoretical, methodological, and application-oriented perspectives, strengthening the conceptual and practical connections between graphs and AI.

Topics of interest include, but are not limited to:

• Graphs in neural architectures: modeling dependencies and computational structures in neural models.

• Graph-based attention and representation learning: leveraging relational structures in attention mechanisms and embeddings.

• Knowledge Graphs and reasoning: construction, integration, and use for symbolic and neuro-symbolic AI.

• Graph-driven interpretability and explainability: using graphs to analyze and interpret model behavior.

• Graphs for trustworthy retrieval: integration of large language models with knowledge graphs and graph-structured retrieval for reliable and explainable search.

• Graphs in foundation model analysis: representing attention patterns and latent spaces as graphs to study token and patch relationships, pruning, and coarsening.

• Graph-based Retrieval-Augmented Generation (RAG): enhancing retrieval, grounding, and response synthesis through graph-structured memory and knowledge integration.

• Temporal, dynamic, and streaming graphs: modeling evolving structures for long-term memory and continual learning.

• Graphs in multi-agent and multi-modal systems: representing interactions and coordination across agents, modalities, and environments.

• Neuro-symbolic integration: connecting graph-based symbolic reasoning with neural computation.

• Large-scale representation learning: scalable graph-based methods for learning over heterogeneous data.

• Theoretical foundations: formal and mathematical frameworks linking graph theory with emerging AI paradigms.

Important Dates

All submission deadlines are end-of-day in Anywhere on Earth (AoE) time zone.