Author

Jia-Yu Zhang

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Conference Open access Sep 2026

Multi-Semantic Aware Self-Supervised Learning for Multi-Label Node Classification

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

Preprint Jul 2026

Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning

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