高级检索

    模体感知的多视图协同聚类优化算法

    Motif-Aware Multi-View Cooperative Clustering Optimization Algorithm

    • 摘要: 图神经网络通过迭代聚合邻域特征学习图的嵌入表示,已广泛应用于图数据分析。现有方法主要关注低阶点边交互,而对以模体为载体的高阶成组交互模式关注不足,导致复杂网络中的高阶依赖关系难以被充分捕捉。模体作为网络中频繁出现的功能性子结构,能够有效揭示节点间的高阶语义关联,而模体共现视图则为刻画此类交互模式提供了新的表征视角。然而,模体共现视图的弱连通性限制了图神经网络的消息传递能力,影响全局信息的有效传播。针对此,提出模体感知的多视图协同聚类优化算法MMCC (Motif-Aware Multi-View Cooperative Clustering Optimization Algorithm),通过自适应多视图融合机制充分挖掘高阶拓扑信息,同时利用对比学习增强不同视图间的表征一致性,从而缓解消息传递受限问题。具体而言,MMCC首先基于不同模体构建多个模体共现视图,并设计基于语义注意力的多视图自编码器动态学习不同模体视图的重要性,实现各视图的自适应融合;其次,引入对比学习约束原始视图与模体共现视图的嵌入空间一致性,缓解因模体共现视图弱连通性导致的消息传递受限问题;最后,通过优化基于KL散度的目标函数,实现特征学习与聚类任务的联合优化。在7个真实网络数据集上的聚类结果表明,MMCC在准确率(ACC)、标准化互信息(NMI)、F1分数(F1)和调整兰德系数(ARI)上较8个基准算法展现显著优势。

       

      Abstract: Graph neural networks (GNNs) learn graph embeddings by iteratively aggregating neighborhood features and have been widely applied in graph data analysis. Existing methods primarily focus on low-order node-edge interactions while overlooking high-order group interaction patterns captured by motifs. This limitation hinders the effective modeling of high-order dependencies in complex networks. Motifs, as frequently occurring functional substructures in networks, can effectively reveal high-order semantic relationships among nodes. The motif co-occurrence view provides a novel perspective for representing such interactions. However, the weak connectivity of motif co-occurrence views restricts message passing in GNNs, thereby impeding the effective propagation of global information. To address this issue, we propose a Motif-Aware Multi-View Cooperative Clustering Optimization Algorithm (MMCC), which fully exploits high-order topological information through an adaptive multi-view fusion mechanism while enhancing representation consistency across different views via contrastive learning to alleviate the issue of restricted message passing. Specifically, MMCC first constructs multiple motif co-occurrence views based on different motif structures and employs a semantic attention-based multi-view auto-encoder to dynamically learn the importance of different motif views, achieving adaptive fusion. Then, contrastive learning is introduced to enforce embedding space consistency between the original view and motif co-occurrence views, mitigating the message passing limitations caused by weak connectivity. Finally, by optimizing a KL-divergence-based objective function, MMCC jointly optimizes feature learning and clustering tasks. Experimental results on seven real-world networks demonstrate that MMCC outperforms eight baseline algorithms in clustering accuracy (ACC), normalized mutual information (NMI), F1-score (F1), and adjusted Rand index (ARI), highlighting its effectiveness in high-order network clustering.

       

    /

    返回文章
    返回