Graph-level clusteringGraph kernelIn this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups
优势:可以保留图的层次结构信息;对图的尺度、大小具有适应性。 代表性方法包括:层次聚合(Hierarchical Clustering);全局排序(Global Top-K);注意力汇聚(Global Attention)。 5. 传统图级学习方法(Traditional Learning) 传统图级学习方法的核心思想是 “手工构造图的特征表示”,将图转化为向量,再送入经典分类器(如SV...
Tsitsulin A, Palowitch J, Perozzi B, Müller E (2023) Graph clustering with graph neural networks. J Mach Learn Res 24(127):1–21 MathSciNet Google Scholar Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. Preprint at arXiv:1710....
Graph Level Predictions on ADNI Data with Hierarchical Clustering Rvosuke 人工智能在读本科生。 来自专栏 · 图表示学习 2 人赞同了该文章 概览 蛋白质共表达图传统上被用于识别阿尔茨海默病(AD)的新生物标志物和阐明生物机制。在这个项目中,我们的目标是使用基于个体蛋白质表达配置文件的GNN来预测疾病状态。我们...
Notably, node clustering pooling methods have limitations regarding storage complexity due to the computation of dense soft-assignment matrices. In contrast, node drop pooling methods are memory-efficient and better suited for large-scale graphs, although they may result in some information loss. For ...
Djidjev H. A fast multilevel algorithm for graph clustering and community detection. ArXiv preprint arXiv:07072387. 2007;.H. Djidjev, A fast multilevel algorithm for graph clustering and community detection, Al- gorithms and Models for the Web-Graph, Lecture Notes in Computer Science, vol....
Several recent visualization techniques, suchas BubbleSets, LineSets and GMap, make explicit use of grouping and clustering, but evaluating such visualizationshas been difficult due to the lack of standardized group-level tasks. With this in mind, our goal is to define anew set of tasks that ...
5.2.2. Node clustering In the node clustering task, for the Photo, Computers, and CS datasets, we report the results when the ratio of training set, validation set, and test set is 8:1:1. We report the performance of node clustering in Table 5. We bold the best method in each colum...
Then, spectral clustering was used to separate the graph and form a functional subgraph. Finally, PageRank was used to calculate the prioritization for each subgraph's indicator, and the two-level indicator system was established. To verify the performance, we took China's 25 smart cities as ...
Infomap clustering finds the community structures that minimize the expected description length of a random walk trajectory; algorithms for infomap clustering run fast in practice for large graphs. In this paper we leverage the effectiveness of Infomap c