FoGE: Fock Space inspired encoding for graph prompting
Sotirios Panagiotis Chytas, Rudrasis Chakraborty, Vikas Singh

TL;DR
This paper introduces FoGE, a novel graph encoding method inspired by Fock space from physics, which enhances large language models' ability to understand and answer questions about various structured graph data with minimal modifications.
Contribution
The work presents a simple, versatile, and parameter-free graph encoder based on Fock space representations that improves graph question-answering across diverse graph types using pre-trained LLMs.
Findings
Effective encoding of various graph types including proteins and hypergraphs.
Simplifies existing graph prompting methods with minimal architectural changes.
Achieves strong performance without extensive fine-tuning or supervision.
Abstract
Recent results show that modern Large Language Models (LLM) are indeed capable of understanding and answering questions about structured data such as graphs. This new paradigm can lead to solutions that require less supervision while, at the same time, providing a model that can generalize and answer questions beyond the training labels. Existing proposals often use some description of the graph to create an ``augmented'' prompt fed to the LLM. For a chosen class of graphs, if a well-tailored graph encoder is deployed to play together with a pre-trained LLM, the model can answer graph-related questions well. Existing solutions to graph-based prompts range from graph serialization to graph transformers. In this work, we show that the use of a parameter-free graph encoder based on Fock space representations, a concept borrowed from mathematical physics, is remarkably versatile in this…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques
