Graph Clustering with Graph Neural NetworksTsitsulin, AntonPalowitch, JohnPerozzi, BryanMüller, EmmanuelJournal of Machine Learning Research
其中GNN 和 MLP 是根据无监督损失函数共同优化的,左边的损失函数鼓励类间的节点尽可能的接近,右边则是鼓励类间正交且每个类具有相同数量的节点。 可以得出,该池化不是仅对节点进行删除操作,而是对graph进行了聚类,类间的邻接矩阵和特征矩阵全都改变了。但是此模型却优于谱聚类,因为谱聚类仅仅考虑了邻接矩阵,而该框...
傅里叶变换中的基e^{2\pi ix\cdot v}就是Laplacian算法的一组特征向量，对于传统傅里叶变换和graph...
Supervised jet clustering with graph neural networks for Lorentz boosted bosons. Phys. Rev. D 102, 075014 (2020). Article ADS Google Scholar Verma, Y. & Jena, S. Jet characterization in heavy ion collisions by QCD-aware graph neural networks. Preprint at https://doi.org/10.48550/arxiv....
In particular, we propose the use of the k-means clustering algorithm [53–56] and Graph Neural Networks (GNNs) [57–61], the latter being deep learning architectures specifically meant to work with graph-structured data. The problem of mesh agglomeration can be re-framed as a graph ...
13. MuchGNN:Multi-Channel Graph Neural NetworksIJCAI 20201. Graph ClassificationNonePTC, DD, PROTEINS, COLLAB, IMDB-BINARY, IMDB-MULTI, REDDIT-MULTI-12K 12. MinCutPool:Spectral Clustering with Graph Neural Networks for Graph PoolingICML 20201. Graph Classification 2. Graph Regression1.PyTorch-Geom...
graph-clustering multiview graphneuralnetwork Updated Mar 25, 2023 Python Namkyeong / BGRL_Pytorch Star 78 Code Issues Pull requests Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping" graph pytorch representation-learning graphneuralnetwork Updated Dec 10, ...
Graph clustering (or community detection) has long drawn enormous attention from the research on web mining and information networks. Recent literature on this topic has reached a consensus that node contents and link structures should be integrated for reliable graph clustering, especially in an unsup...
Despite the potential of the information on spatial location for improving the accuracy of clustering, the above-mentioned algorithms of spatial clustering have not achieved optimal performance [24], [25]. Graph neural networks (GNNs) have become popular in the recent literature because they can ...
我们提出了一个层次图神经网络(GNN)模型,该模型学习如何使用一组图像训练集,将一组图像聚类成未知数量的身份,该训练集使用属于不相交身份集的标签进行注释。我们的分层 GNN 使用一种新颖的方法来合并在层次结构的每个级别预测的连接组件,以在下一个级别形成一个新图。与完全无监督的层次聚类不同,分组和复杂性标准的...