Deep clustering, which can elegantly exploit data representation to seek a partition of the samples, has attracted intensive attention. Recently, combining auto-encoder (AE) with graph neural networks (GNNs) has accomplished excellent performance by introducing structural information implied among data in clustering tasks. However, we observe that there are some limitations of most existing works: 1) in practical graph datasets, there exist some noisy or inaccurate connections among nodes, which would confuse network learning and cause biased representations, thus leading to unsatisfied clustering performance; 2) lacking dynamic information fusion module to carefully combine and refine the node attributes and the graph structural information to learn more consistent representations; and 3) failing to exploit the two separated views’ information for generating a more robust target distribution. To solve these problems, we propose a novel method termed deep fusion clustering network with reliable structure preservation (DFCN-RSP). Specifically, the random walk mechanism is introduced to boost the reliability of the original graph structure by measuring localized structure similarities among nodes. It can simultaneously filter out noisy connections and supplement reliable connections in the original graph. Moreover, we provide a transformer-based graph auto-encoder (TGAE) that can use a self-attention mechanism with the localized structure similarity information to fine-tune the fused topology structure among nodes layer by layer. Furthermore, we provide a dynamic cross-modality fusion strategy to combine the representations learned from both TGAE and AE. Also, we design a triplet self-supervision strategy and a target distribution generation measure to explore the cross-modality information. The experimental results on five public benchmark datasets reflect that DFCN-RSP is more competitive than the state-of-the-art deep clustering algorithms. The corresponding code is available at https://github.com/gongleii/DFCN-RSP.

中文翻译：

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具有可靠结构保存的深度融合聚类网络


深度聚类可以优雅地利用数据表示来寻求样本的划分，引起了人们的广泛关注。最近，将自动编码器（AE）与图神经网络（GNN）相结合，通过在聚类任务中引入数据隐含的结构信息，取得了优异的性能。然而，我们观察到大多数现有工作都存在一些局限性：1）在实际的图数据集中，节点之间存在一些噪声或不准确的连接，这会混淆网络学习并导致有偏差的表示，从而导致聚类性能不理想； 2）缺乏动态信息融合模块来仔细组合和细化节点属性和图结构信息以学习更一致的表示； 3) 未能利用两个分离视图的信息来生成更稳健的目标分布。为了解决这些问题，我们提出了一种称为具有可靠结构保存的深度融合聚类网络（DFCN-RSP）的新方法。具体来说，引入随机游走机制，通过测量节点之间的局部结构相似性来提高原始图结构的可靠性。它可以同时过滤掉噪声连接并补充原始图中的可靠连接。此外，我们提供了一种基于变压器的图自动编码器（TGAE），它可以使用具有局部结构相似性信息的自注意力机制来逐层微调节点之间的融合拓扑结构。此外，我们提供了一种动态的跨模态融合策略来结合从 TGAE 和 AE 学习到的表示。 此外，我们设计了三重态自我监督策略和目标分布生成措施来探索跨模态信息。在五个公共基准数据集上的实验结果表明，DFCN-RSP 比最先进的深度聚类算法更具竞争力。相应的代码可以在https://github.com/gongleii/DFCN-RSP获取。