To address these issues, this study proposes a novel single-view attributed graph clustering model - S2M, which learns effective multi-view feature embeddings. We propose a flexible and controllable data enhanc
Multi-view attributed graph clustering based on graph diffusion convolution with adaptive fusion论文阅读 gauge 1.论文背景 图聚类是数据挖掘中流行的技术之一,广泛应用于社交网络分析、推荐系统和异常检测中。 现有的基于多视图属性图的方法忽略了不同视图之间的差异和视图之间的一致性。 为此,本文提出了一种基于...
1.论文背景多视图图聚类在过去的几年中得到了广泛的研究。然而,现有的方法在两个主要方面仍然有局限性: (1)它们中的大多数不能处理同时具有属性和图的数据。目前,多视图属性图数据普遍存在,对有效的聚类方法…
Multi-view graph clustering has been intensively investigated during the past years. However, existing methods are still limited in two main aspects. On the one hand, most of them can not deal with data that have both attributes and graphs. Nowadays, multi-view attributed graph data are ...
论文地址:Multi-View Attribute Graph Convolution Networks for Clustering | IJCAI 论文代码:MAGCN 1.多视图属性聚类:MAGCN 1.存在问题:GNN 融入Multi-View Graph 1)他们不能将指定学习的不同权重的分配给邻域内的不同节点; 2)他们可能忽略了进行节点属性和图结构的重构以提高鲁棒性; ...
Multi-view attributed graph clustering. IEEE Transactions on Knowledge and Data Engineering, 2021. 2 [29] Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Xifeng Guo, Marius Kloft, and Liangzhong He. Multiview subspace clustering via co-training robust data representat...
Over the past few years, multiview attributed graph clustering has achieved promising performance via various data augmentation strategies. However, we observe that the aggregation of node information in multilayer graph autoencoder (GAE) is prone to deviation, especially when edges or node attributes ...
多图聚类模型(Graph-based Multi-view Clustering, GMC)是一种专门设计用于处理多视图数据的聚类算法,它利用图结构来捕捉数据点之间的关系,并通过联合优化多个视图的图表示来达到更准确的聚类效果。 GMC算法的核心在于能够有效融合不同来源的信息,即使这些信息可能存在矛盾或不完整,也能从中提取出一致的聚类结构。
论文标题:Multi-view Contrastive Graph Clustering论文作者:Erlin Pan、Zhao Kang论文来源:2021, NeurIPS论文地址:download论文代码:download1 介绍本文贡献:使用Graph Filter 过滤了高阶噪声数据; 提出Graph Contrastive Regularizer 改善了视图的质量; 2 方法
1. We propose an NMF-based multi-view clustering model that introduces orthogonal constraint and graph regularization into coefficient matrices to focus on useful information about true clustering structures. 2. We verify the effectiveness of the proposed multi-view clustering algorithm by conducting exte...