Title: Two Sides of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Primary area: foundation or frontier models, including LLMs
Keywords: Foundation Model; Graph Pre-training; Vector Quantization

Abstract: Graph foundation models (GFMs), inspired by the success of LLMs, are designed to learn the optimal embedding function from multi-domain text-attributed graphs (pre-training) for the downstream cross-task generalization capability (fine-tuning). During our investigation, graph vector quantized-masked autoencoder (gVQ-MAE) stands out among the increasingly diverse landscape of GFM architectures. This is attributed to its ability to jointly encode topology and textual attributes from multiple domains into discrete embedding spaces with clear semantic boundaries. Despite its potential, domain generalization conflicts cause imperceptible pitfalls. In this paper, we instantiate two of them, and they are just like two sides of the same GFM optimization coin - Side 1 Model Degradation: The encoder and codebook fail to capture the diversity of inputs (e.g., social networks and molecular graphs); Side 2 Representation Collapse: The hidden embedding and codebook vector fail to preserve semantic separability due to constraints from narrow representation subspaces. These two pitfalls (sides) collectively impair the decoder and generate the low-quality reconstructed supervision, causing the GFM optimization dilemma during pre-training (coin). Through empirical investigation, we attribute the above challenges to Information Bottleneck and Regularization Deficit. To address them, we propose MoT (Mixture-of-Tinkers) - (1) Information Tinker for Two Pitfalls, which utilizes an edge-wise semantic fusion strategy and a mixture-of-codebooks with domain-aware routing to improve information capacity. (2) Regularization Tinker for Optimization Coin, which utilizes two additional regularizations to further improve gradient supervision in our proposed Information Tinker. Notably, as a flexible architecture, MoT adheres to the scaling laws of GFM, offering a controllable model scale. Compared to SOTA baselines, experiments on 22 datasets across 6 domains demonstrate that MoT achieves significant improvements in supervised (1.4%), few-shot (3.1%), and zero-shot (3.3%) scenarios.

TL;DR: We propose MoT to address optimization pitfalls in GFMs and achieve SOTA cross-domain generalization.