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...
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....
As a unique non-Euclidean* data structure for machine learning, graph analysis focuses on node classification, link prediction, and clustering.Graph neural networks (GNNs) are deep learning based methods that operate on graph domain. *数据可以分两大类:欧几里得数据和非欧几里得数据。欧几里得数据的特点...
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...
: Graph Clustering with Graph Neural Networks (CoRR 2020) [Example] Graclus Pooling from Dhillon et al.: Weighted Graph Cuts without Eigenvectors: A Multilevel Approach (PAMI 2007) [Example] Voxel Grid Pooling from, e.g., Simonovsky and Komodakis: Dynamic Edge-Conditioned Filters in ...
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...
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 ...
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...
Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph contrastive clustering methods are manual, which...