GFT: Graph Foundation Model with Transferable Tree Vocabulary
Zehong Wang, Zheyuan Zhang, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye

TL;DR
This paper introduces GFT, a graph foundation model that uses transferable computation trees as a vocabulary to improve generalization across various graph tasks and domains.
Contribution
We propose a novel GFT model that encodes transferable computation trees as tokens, enabling cross-task and cross-domain transferability in graph learning.
Findings
GFT demonstrates improved generalization across multiple graph tasks.
Transferable computation trees are effective as a shared vocabulary.
Theoretical analysis confirms the transferability of computation trees.
Abstract
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such as scientific research, social network analysis, drug discovery, and e-commerce. Despite the significant progress of pre-trained graph neural networks, there haven't been GFMs that can achieve desired performance on various graph-learning-related tasks. Building GFMs may rely on a vocabulary that encodes transferable patterns shared among different tasks and domains. Unlike image and text, defining such transferable patterns for graphs remains an open question. In this paper, we aim to bridge this gap by rethinking the transferable patterns on graphs as computation trees -- i.e., tree structures derived from the message-passing process.…
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Taxonomy
TopicsSemantic Web and Ontologies · Natural Language Processing Techniques · Graph Theory and Algorithms
