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OpenGraph: Towards Open Graph Foundation Models (2403.01121v4)

Published 2 Mar 2024 in cs.LG, cs.AI, and cs.SI
OpenGraph: Towards Open Graph Foundation Models

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 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.

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Enhancing Graph Learning Paradigm with OpenGraph: Insights into Zero-Shot Performance and Scalability

Key Contributions and Methodology

The landscape of graph learning tasks, ranging from link prediction in recommender systems to node classification in citation networks, has seen a pivotal shift with the introduction of Graph Neural Networks (GNNs). These networks excel in encoding complex relational data within a graph's structure, thereby setting a new benchmark for performance in numerous applications. Despite their success, GNNs often stumble when faced with generalizing to graph data unseen during training, a significant bottleneck that limits their potential applications.

Addressing this challenge, the paper on the OpenGraph (\model) model presents a landscape-altering approach aimed at transcending these limitations. Through the development of a general graph foundation model, this work stands out by efficiently interpreting complex topological structures across a wide spectrum of graph data, thereby markedly enhancing zero-shot learning capabilities across diverse domains. Key technical challenges tackled in this work include the varying node token set shifts, efficient node-wise dependency modeling, and addressing domain-specific data scarcity.

The \model\ introduces three cardinal innovations to overcome these hurdles:

  1. Unified Graph Tokenizer: A first-of-its-kind technique focusing on the creation of universal graph tokens, effectively addressing the challenge of node token set shifts between different graphs. This tokenizer transforms any input graph into a unified sequence of tokens, embedding the rich topology-aware features with minimal loss of structural information.
  2. Scalable Graph Transformer: Pioneering a graph transformer architecture that leverages efficient self-attention mechanisms with anchor sampling, this model component ensures the scalability of node-wise dependency encoding within the graphs.
  3. LLM-Enhanced Data Augmentation: By integrating LLMs for synthetic graph generation, this method significantly alleviates the issue of domain-specific data scarcity. It enriches the model's training data with diversified and realistic graph scenarios, preparing the model for robust zero-shot learning.

Empirical Evaluation and Findings

Extensive experiments validate the \model's commendable zero-shot learning performance across various task settings and domains. Notably, when compared to established baselines in both one-shot and five-shot learning cases, \model\ showcases superior generalization capabilities, attributed to its advanced graph tokenizer and transformer architecture. Moreover, the paper critically evaluates the impact of different model configurations, revealing insights into the scalability and efficiency of the proposed model.

Implications and Future Directions

The advancements presented in the OpenGraph model have far-reaching implications for the field of graph learning. By effectively bridging the gap between pre-training and generalization across unseen datasets, this work opens avenues for developing more sophisticated and versatile graph foundation models. Moving forward, it is anticipated that further exploration could focus on enhancing the model's interpretability and adaptability, potentially extending to dynamic and temporal graph data for broader applications.

In the grand scheme of AI research, \model\ not only contributes to the progression of graph learning methodologies but also sets a precedent for leveraging the synergy between tokenization, transformer-based architectures, and synthetic data augmentation strategies. As we look to the future, the methodologies refined and introduced by \model\ hold the promise of unlocking new realms of possibilities within and beyond graph-based data analysis.

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Authors (3)
  1. Lianghao Xia (65 papers)
  2. Ben Kao (17 papers)
  3. Chao Huang (244 papers)
Citations (19)
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