A graph may be generated that graphically depicts the various category correlations and/or user correlations as determined based on a set of network data. Clustering is performed to determine cluster(s) of users who are (e.g., strongly) correlated and similar to one another with regard to ...
Graph data models are useful to store, process and analyse highly interlinked data [1]. This is achieved through the use of graph theory to store data in the form of nodes and edges [2]. With the recent rise in the popularity of graph databases, which rely on graph data models to stor...
A cluster in a graph is a set of nodes that has more connections within the set than outside the set [7]. Clustering can be useful for understanding large networks where the numbers of nodes and edges are too large for a human analyst to examine individually. Dividing the network into ...
Clustering spatial transcriptomics data. Bioinformatics 38, 997–1004 (2022). Article Google Scholar Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–...
Linkage Based Face Clustering via Graph Convolution Network Abstract 在本文中,我们为人脸聚类任务提出了一种精确和可扩展的方法。我们的目标是将一组人脸按照他们潜在的身份进行分组。我们将此任务表述为一个连接预测问题:如果两个人脸具有相同的身份,那么它们之间存在连接。关键的思想是,我们在特征空间中找到一个实例...
and human-made complex systems. Here we show how the network geometry paradigm can be extended to the case of directed networks. We define a maximum entropy ensemble of random geometric directed graphs with a given sequence of in-degrees and out-degrees. Beyond these local properties, the ...
这篇文章是在《Attention-driven Graph Clustering Network》的基础上进行优化,模型的基础结构没变,增加了一个分布融合模块,以及新型的双重自监督,为什么说新,接下来会详细解释。 与AGCN相似的内容就不再介绍,下面详细介绍分布融合模块和双重自监督模块。分布融合模块 顾名思义,分布融合就是将不同的分布通过某种方式结...
论文阅读06——《CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering》 Ideas: Model: 交叉注意力融合模块 图自编码器 Ideas: 提出一种基于端到端的交叉注意力融合的深度聚类框架,其中交叉注意力融合模块创造性地将图卷积自编码器模块和自编码器模块多层级连起来 ...
This paper is devoted to the topic of evaluation measures for overlapping graph clusterings. In particular, we proposed three methods to adapt existing crisp evaluation measures to handle Tatiana Gossen is a research assistant at the “Data and Knowledge Engineering” (DKE) group, Otto-von-...
Chattopadhyay A, Hassanzadeh P, Pasha S (2020) Predicting clustered weather patterns: a test case for applications of convolutional neural networks to spatio-temporal climate data. Sci Rep 10:1–13 Google Scholar Chen H, Yu Z, Yang Q, Shao J (2020) Attributed graph clustering with subspace ...