基于对比硬采样的无监督节点聚类
Unsupervised node clustering via contrastive hard sampling
AI总结：
针对传统无监督节点聚类忽略类别不变特征的问题，本文提出细粒度对比学习框架，通过反事实增强构造虚拟节点生成高质量硬负样本对，在多个数据集和基线上实现聚类性能显著提升。
摘要：
本文提出一种用于无监督节点聚类的细粒度对比学习方案。以往的聚类方法仅关注一小部分具有明确聚类特征的特征集（类别依赖特征），而忽略了其余的特征空间（类别不变特征）。本文通过图对比学习利用类别不变特征，为无监督聚类挖掘更多高质量特征。我们构建了一种新颖的面向自监督学习的节点级细粒度增强框架，该框架从整个特征空间中迭代筛选具有竞争力的对比样本，以节点关系的正例和负例形式呈现。节点关系的正例通常以图同质性中的边来表示，而负例则是隐含的，不存在直接连边。但我们发现，仅从局部邻域之外采样节点会得到竞争力较弱的负样本对，对对比学习的效果提升有限。受反事实增强的启发，我们转而通过构建虚拟节点来采样具有竞争力的负节点关系：这些虚拟节点以自监督的方式继承类别不变特征，同时改变类别依赖特征，从而生成更靠近决策边界的对比对，提供更优的对比效果。最终，我们的实验在6个基线模型和6个真实社交网络数据集上的有监督节点聚类任务中取得了显著提升。
英文摘要：
This paper introduces a fine-grained contrastive learning scheme for unsupervised node clustering. Previous clustering methods only focus on a small feature set (class-dependent features), which demonstrates explicit clustering characteristics, ignoring the rest of the feature spaces (class-invariant features). This paper exploits class-invariant features via graph contrastive learning to discover additional high-quality features for unsupervised clustering. We formulate a novel node-level fine-grained augmentation framework for self-supervised learning, which iteratively identifies competitive contrastive samples from the whole feature spaces, in the form of positive and negative examples of node relations. While positive examples of node relations are usually expressed as edges in graph homophily, negative examples are implicit without a direct edge. We show, however, that simply sampling nodes beyond the local neighborhood results in less competitive negative pairs, that are less effective for contrastive learning. Inspired by counterfactual augmentation, we instead sample competitive negative node relations by creating virtual nodes that inherit (in a self-supervised fashion) class-invariant features, while altering class-dependent features, creating contrasting pairs that lie closer to the boundary and offering better contrast. Consequently, our experiments demonstrate significant improvements in supervised node clustering tasks on six baselines and six real-world social network datasets.
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。
