Graph Reasoning with LLMs (GReaL)

Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications. Large Language Models (LLMs) have demonstrated impressive capabilities by advancing state-of-the-art on many language-based benchmarks. Their ability to process and understand natural language open exciting possibilities in various domains. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with LLMs remains an understudied problem that has recently gained more attention.This tutorial builds upon recent advances in expressing reasoning problems through the lens of tasks on graph data. The first part of the tutorial will provide an in-depth discussion of techniques for representing graphs as inputs to LLMs. The second, hands-on, portion will demonstrate these techniques in a practical setting. As a learning outcome of participating in the tutorial, participants will be able to analyze graphs either on free-tier Colab or their local machines with the help of LLMs.