发表机构
Northeastern University; Southeast University（东北大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图数据中节点属性与结构同时缺失的聚类难题，提出RGCN网络，通过视图解耦双分支插补、多超球面混合先验及边界感知对比增强，有效提升聚类性能。
AI 中文摘要
在节点属性和结构链接均部分缺失的图上进行聚类仍然是一项具有挑战性的任务。现有方法通常依赖于在单视图缺失的不完整图上进行“插补后聚类”，这种方法在属性和结构同时缺失的情况下，容易受到跨视图错误传播和聚类边界模糊的影响。为了解决这些局限性，我们提出了一种用于多重缺失数据的鲁棒图聚类网络（RGCN），该网络旨在处理节点属性和图结构同时不完整的情况。RGCN引入了三项关键创新：首先，我们设计了一种视图解耦的双分支插补方法，以减轻干扰并在恢复缺失数据时实现相互增强。其次，我们采用多超球面混合先验，以增强方向性潜在流形上的簇内紧凑性和簇间可分离性。第三，边界感知的对比增强目标减轻了由插补偏差引起的聚类模糊。在真实世界数据集上的大量实验表明，RGCN在各种缺失模式下始终优于最先进的基线方法。
英文摘要
Clustering on graphs where both node attributes and structural links are partially missing remains a challenging task. Existing methods typically rely on imputation-then-clustering on single-view missingness incomplete graphs, which are vulnerable to cross-view error propagation and cluster-boundary blurring under simultaneous attribute and structure missingness. To address these limitations, we propose a Robust Graph Clustering Network for Multiple Missing Data (RGCN), which is designed to handle simultaneous node attribute and graph structure incompleteness. RGCN introduces three key innovations: First, we design a view-decoupled dual-branch imputation to mitigate interference and enable mutual enhancement in recovering missing data. Second, we employ a multi-hyperspherical mixture prior to enhance intra-cluster compactness and inter-cluster separability on a directional latent manifold. Third, a boundary-aware contrastive enhancement objective mitigates the blurring of clusters caused by imputation bias. Extensive experiments on real-world datasets demonstrate that RGCN consistently outperforms state-of-the-art baselines under various missing patterns.
