Therefore, this paper introduces a novel graph embedding-based deep multi-view clustering algorithm. This algorithm employs a co-training strategy to sustain the execution of the global clustering task and intr
Deep multi-view graph clustering network with weighting mechanism and collaborative training阅读笔记 gauge 2 人赞同了该文章 1.论文背景 随着图卷积网络(GCN)在图嵌入学习中具有强大的功能,同时能够捕获节点特征信息的发展,基于图自编码器的深度多视点图聚类方法已成为一种新的流。虽然他们达到满意的性能,他们仍...
Graph Filter-based Multi-view Attributed Graph Clustering[C]//IJCAI. 2021: 2723-2729. 【背景】 本文主要做的的属性图的聚类问题,当前的针对于multi-view的图表示学习方法主要可以被分为两个类别: 将多视图的数据映射到一个图中,然后使用单视图的算法。 通过一些表示学习方法来获得节点embedding之后,然后使用...
workflow.add_node("gnn", gnn_agent) workflow.add_node("clustering", clustering_agent)# 定义处理流程的执行顺序workflow.add_edge("preprocess","graph") workflow.add_edge("graph","embedding") workflow.add_edge("embedding","gnn") workflow.add_edge("gnn","clustering") workflow.set_entry_point(...
论文标题:Deep Graph Clustering via Mutual Information Maximization and Mixture Model论文作者:Maedeh Ahmadi, Mehran Safayani, Abdolreza Mirzaei论文来源:2022, arXiv 论文地址:download 论文代码:download 1 Introduction结合高斯混合模型+对比学习。2 Method...
这篇博文主要是对论文“Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding”的整理总结,这篇文章将图嵌入与概率深度高斯混合模型相结合,使网络学习到符合全局模型和局部结构约束的强大特征表示。将样本作为图上的节点,并最小化它们的后验分布之间的加权距离,在这里使用Jenson-Shannon散度...
graph-clusteringspectral-clusteringgraph-classificationgraph-neural-networksgraph-coarseninggraph-poolingnode-clustering UpdatedApr 28, 2025 Python cszhangzhen/MVPool Star23 Code Issues Pull requests Hierarchical Multi-View Graph Pooling with Structure Learning (TKDE-2021) ...
论文阅读02——《Attributed Graph Clustering: A Deep Attentional Embedding Approach》 Ideas: Model: Two-step DAEGC 图注意力自动编码器 自训练聚类模块 具体算法流程 Ideas: Two-step的图嵌入方法不是目标导向的,聚类效果不好,提出一种基于目标导向的属性图聚类框架。
论文阅读07——《Deep Attention-guided Graph Clustering with Dual Self-supervision》 Ideas: Model: 分布融合模块 双重自监督模块 软自监督(SSS) 硬自监督(HSS) Ideas: 作者认为之前的深度聚类方法有以下四个缺陷: 它们简单地将节点内容和拓扑结构信息的重要性等同起来; ...
Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus[J]. International Journal of Geographical Information Science, 2023: 1-27. Link Karim S, Mehmud M, Alamgir Z, et al. Dynamic Spatial Correlation in Graph WaveNet for Road ...