Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
arXiv:2602.13075v2 Announce Type: replace Abstract: Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most existing methods are designed for either homogeneous or heterogeneous graphs, thereby hindering unified graph modeling across diverse graph types. This separation contradicts real-world applications, where mixed homogeneous and heterogeneous graphs are ubiquitous, and distribution shifts between upstream pre-training and downstream deployment are common. In this paper, we empirically demonstrate
The paper addresses a current limitation in graph pre-training, where models struggle with the prevalent real-world mixture of homogeneous and heterogeneous graph data and distribution shifts between training and deployment.
This research provides a pathway to more robust and versatile graph neural networks, crucial for AI applications across diverse and complex data structures, potentially accelerating AI development and deployment.
The development of unified pre-training methods for both homogeneous and heterogeneous graphs will lead to more effective and generalizable graph-based AI models, reducing the need for specialized models for different graph types.
- · AI developers
- · Graph AI researchers
- · Companies using graph data (e.g., social networks, knowledge graphs)
- · Cloud AI infrastructure providers
- · Developers of highly specialized, single-domain graph models
Improved performance and broader applicability of graph neural networks in various AI tasks.
Accelerated development of AI systems leveraging complex, multi-modal data representations.
Enhanced AI capabilities for tasks like fraud detection, drug discovery, and recommendation systems, leading to economic and societal impacts.
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Read at arXiv cs.LG