arXiv 2502.084322025-02-12cs.LG

通过共性拉近：利用共享组增强超图对比学习

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；School of Computer Engineering, Hansung University（韩星大学计算机工程学院）；Department of Industrial and Systems Engineering, KAIST（韩国科学技术院工业与系统工程系）

Daeyoung Roh, Donghee Han, Daehee Kim, Keejun Han, Mun Yi

备注：: 11page, 5 figures, 6 tables, 2024 IEEE International Conference on Big Data

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AI总结：

针对超图对比学习研究少、现有方法无法充分利用超图高阶关联信息的问题，提出HyFi方法，通过节点特征加噪增强、引入弱正样本细粒度划分正样本，在10个数据集节点分类任务中平均排名优于基线，且训练高效。

摘要：

超图为表示现实世界中常以组形式发生的交互中的复杂多维关系提供了优越的建模框架，克服了传统同质图的局限性。然而，基于超图的对比学习研究较少，现有基于图的对比学习方法无法充分利用超图中的高阶关联信息。本文提出超图细粒度对比学习（HyFi）方法，旨在挖掘超图固有的复杂高维信息。在避免破坏超图拓扑的传统图增强方法的同时，该方法通过向节点特征添加噪声提供了简单高效的学习增强函数。此外，我们通过引入弱正样本这一新关系，突破了对比学习中正负样本的传统二分关系，证明了细粒度划分正样本在对比学习中的重要性。因此，HyFi能够生成高质量嵌入，在10个数据集的节点分类任务中平均排名优于监督和无监督基线。我们的方法有效利用了高维超图信息，相比现有基于图的对比学习方法表现出显著改进，且在训练速度和GPU内存成本方面具有效率优势。源代码可在https://github.com/Noverse0/HyFi.git获取。

英文摘要：

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraphbased contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the highorder correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce highquality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://github.com/Noverse0/HyFi.git.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。