Graph self-supervised learning aims to mine intrinsic signals from graph data itself to train models. It enables the acquisition of high-quality representations without manual annotations, making it suitable for various label-scarce scenarios and thus garnering substantial interest. Existing graph self-supervised metho...
Jia-Yu Zhang, Ji-Tao Zhao, Dong-Xiao He et al.· Proceedings of the Thirty-Fi...· 0 citations
Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node classification can effectively model multiple labels, while only considering in-domain scenarios where the model needs to be trained and te...
Dongxiao He, Jiayu Zhang, Jitao Zhao et al.· 0 citations
Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks. Real-world graphs associate nodes with text, images, and other modalities, making multimodal graphs essential for representing complex entities and relations. Moreover, collecting labels and ad...
Ankang Yang, Jitao Zhao, Di Jin et al.· arXiv.org· 0 citations
SliGFM is proposed, a graph foundation model built upon topology-aware sliding-window feature encoding and generative reconstruction that enables a smoothness-aware transformer to capture transferable relational patterns among feature tokens within each node, while the generative reconstruction objective encourages pre...
A Propagation-aware Graph Foundation Model (ProGFM), which regards the propagation relationships between edges and feature dimensions as transferable knowledge units, and exhibits superior generalization performance compared with existing methods.
Yi Wang, Jitao Zhao, Di Jin et al.· arXiv.org· 0 citations
This work proposes AgentGFM, in which all node agents follow a shared end-to-end trainable policy rather than using independent models, and describes this capability as information-flow control, which is inspired by recent advances in agent technology.
Jingbo Cui, Jitao Zhao, Di Jin et al.· 0 citations
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