Pith. sign in

REVIEW 5 cited by

OpenGraph: Towards Open Graph Foundation Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.01121 v4 pith:WXGXSIQB submitted 2024-03-02 cs.LG cs.AIcs.SI

Lianghao Xia , Ben Kao , Chao Huang This is my paper · ORCID
classification cs.LGcs.AIcs.SI
keywords graphdatamodelopengraphapproachchallengediversefoundation
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Graph learning has become essential in various domains, including recommendation systems and social network analysis. Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification. However, a key challenge remains: the difficulty of generalizing to unseen graph data with different properties. In this work, we propose a novel graph foundation model, called OpenGraph, to address this challenge. Our approach tackles several technical obstacles. Firstly, we enhance data augmentation using a large language model (LLM) to overcome data scarcity in real-world scenarios. Secondly, we introduce a unified graph tokenizer that enables the model to generalize effectively to diverse graph data, even when encountering unseen properties during training. Thirdly, our developed scalable graph transformer captures node-wise dependencies within the global topological context. Extensive experiments validate the effectiveness of our framework. By adapting OpenGraph to new graph characteristics and comprehending diverse graphs, our approach achieves remarkable zero-shot graph learning performance across various settings. We release the model implementation at https://github.com/HKUDS/OpenGraph.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Attacking Graph Foundation Models Through Their Shared Representation

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A shared representation layer in graph foundation models is a distinct attack surface: input edits break three of six models and one spectral tokenizer is uniquely fragile.

  2. VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation

    cs.CV 2026-03 conditional novelty 6.0 of 10

    VesselTok learns compact continuous tokens of large tubular biomedical graphs from centerline points plus a fixed pseudo-radius, enabling reconstruction, generation, and link prediction across anatomies.

  3. No Need to Train Your RDB Foundation Model

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Column-wise, parameter-free JUICE encodings let single-table ICL models solve multi-table RDB prediction tasks with no training or fine-tuning.

  4. Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning

    cs.CL 2025-06 conditional novelty 6.0 of 10

    MDGCL pre-trains graph encoders on multiple domains using same-domain discrimination and a downstream domain-attention mechanism, outperforming existing text-free graph foundation models.

  5. A Comprehensive Data-centric Overview of Federated Graph Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A data-centric taxonomy for Federated Graph Learning that classifies 79 studies by data characteristics and data utilization, plus a discussion of integration with pre-trained large models.

Pith tools