Build from-scratch graph clustering using Fiedler’s method, maximum modularity, and Laplacian embeddings on synthetic graphs. Partitions and embeddings reveal distinct communities and highlight use cases for spectral methods in Python. - KajIzora/Graph-
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets). - zxy-smart/Awesome-Deep-Graph-Clustering
论文题目:One2Multi Graph Autoencoder for Multi-view Graph Clustering 论文链接:One2Multi Graph Autoencoder for Multi-view Graph Clustering 论文源码:https://github.com/songzuolong/WWW2020-O2MAC 提出背景 前人研究multi-view的方法可以分为两类: 基于图分析方法,最大化不同view之间的某种相互协议,然后将一...
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。
知识图谱构建: 对应的工作流是create_final_entities.py, 翻阅源码可以发现, 该workflow会依赖于workflow:create_base_extracted_entities, 同时定义了cluster_graph, embed_graph等操作, 其中 cluster_graph采用了leiden策略, 具体代码位于index/verbs/graph/clustering/cluster_graph.py, from datashaper import TableCo...
Local Clustering Coefficient:和Vertex Embeddedness类似,也是衡量邻居之间的紧密程度。 2.2 边评估类 SparklingGraph实现了两种边评估算法: 一个是Common Neighbours,即看两个节点的邻居重合个数; 另外一个是Adamic/Adar,这个度量源于2009年在Springer上发表的论文《Predicting missing links via local information》[44],...
These genes may be the collaborators of well-known cancer genes. We also examined the structural features of gene modules with respect to their graphical metrics, including transitivity, clustering coefficients, degree centrality, and betweenness centrality, and we found that the topological structure ...
https://github.com/JinmiaoChenLab/GraphST. 参考文献 Long, Y., Ang, K.S., Li, M. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat Commun 14, 1155 (2023).
思想流程大概是:soft clustering -> super node -> coarsening 3. Node selection 节点选择就是选择一些重要节点去代替原图: 代表论文:self-attention graph pooling 论文链接:https://arxiv.org/pdf/1904.08082.pdf 这个self-attention类似于分析节点的重要性,方法类似节点分类的操作。
DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data. Nat. Commun. 10, 4613 (2019). Article PubMed PubMed Central Google Scholar Demetci, P., Santorella, R., Sandstede, B., Noble, W. S. & Singh, R. SCOT: Single-Cell Multi-Omics ...