RHEA：用于鲁棒多模态属性图聚类的可靠性协调重构与分配
RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering
浏览论文内容
中文总结 AI 辅助
针对多模态属性图聚类中属性含噪或缺失时性能下降的问题，提出RHEA框架，通过估计节点特定模态可靠性并结合邻域信息提升聚类效果，在基准测试中表现优于最强基线。
中文摘要 AI 辅助
多模态属性图（MAG）的节点在关系结构上承载文本、图像等异构属性，已成为无标签实体分组任务（包括社区发现和产品分割）的基础载体。现有MAG聚类方法在属性干净且完整时能有效整合互补模态，但在属性含噪或缺失时性能大幅下降，因为它们隐含假设所有节点的模态可靠性相等。实际中，模态可靠性本质上是节点特定的：图像可能损坏或缺失，文本描述可能不完整或含噪。我们认为，在属性同质性假设下，图邻域自然为估计节点特定模态可靠性提供无监督证据。基于此，我们提出RHEA，一种用于MAG聚类的可靠性感知框架，它从邻域共识中估计节点特定模态可靠性，并将该信号传播到整个聚类流程。RHEA从图邻域重构不可靠或缺失的模态，在可靠性感知融合期间自适应加权各模态，并执行拓扑感知的最优传输聚类，包含可靠性感知传输分配和邻域共识分配提炼。此外，重构表示的置信度被纳入聚类目标，使不确定的重构在优化过程中按比例贡献。在四个MAG基准的五种属性条件下的实验表明，RHEA始终优于最强基线，且NMI增益随属性质量下降而增大。
英文摘要
Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-free entity grouping tasks, including community discovery and product segmentation. Existing MAG clustering methods effectively integrate complementary modalities when attributes are clean and complete, but degrade substantially under noisy or missing attributes because they implicitly assume equal modality reliability across all nodes. In practice, modality reliability is inherently node-specific: images may be corrupted or absent, while textual descriptions are incomplete or noisy. We argue that, under attribute homophily, graph neighborhoods naturally provide supervision-free evidence for estimating node-specific modality reliability. Based on this insight, we propose RHEA, a reliability-aware framework for MAG clustering that estimates node-specific modality reliability from neighborhood consensus and propagates this signal throughout the clustering pipeline. RHEA reconstructs unreliable or missing modalities from graph neighborhoods, adaptively weights modalities during reliability-aware fusion, and performs topology-aware optimal transport clustering with reliability-aware transport assignment and neighbor-consensus assignment distillation. Furthermore, the confidence of reconstructed representations is incorporated into the clustering objective, allowing uncertain reconstructions to contribute proportionally during optimization. Experiments on four MAG benchmarks under five attribute conditions show that RHEA consistently outperforms the strongest baseline, with NMI gains increasing as attribute quality deteriorates.
