Author

Zhengyu Wu

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Preprint Aug 2026

SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering

Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods mostly follow a two-stage paradigm: they first construct a candidate negative pool for each user and then select negative samples from the p...

Jia-Yi Wu, Zheng-Yu Wu, Xun-Kai Li et al. · 0 citations

#machine learning Preprint Oct 2026

No-Free-Graph: Learning When Multimodal Data Should Be Graphified

Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. Existing studies have made substantial progress on how to construct and optimize graphs, but rarely consider a more fundamental question: whether additional relational s...

Ze-Kai Chen, Kai Hu, Yu-Xin Zeng et al. · 0 citations

#machine learning Preprint Sep 2026

MOVE: Multimodal Open-world Verification and Expansion for Graph Learning

Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Existing approaches typically detect unknown nodes and use LLMs to generate candidate class descriptions, but they do not determine whether existing classes are insufficie...

Ze-Kai Chen, Jia-Yang Xing, Xun Wu et al. · 0 citations

Preprint Jul 2026

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

FedGAMMA is proposed, casting federated multimodal graph foundation learning as a two-stage semantic-structural alignment problem of federated pre-training and prompt-based fine-tuning, and outperforms competitive baselines accross multi-domain datasets on multiple tasks.

Xunkai Li, Guohao Fu, Yuming Ai et al. · 0 citations