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...
Automatic selection of clustering algorithms using supervised graph embedding,Noy Cohen-Shapira, Lior Rokach Improving Learning to Branch via Reinforcement Learning,Haoran Sun, Wenbo Chen, Hui Li, Le Song A Practical Guide to Graph Neural Networks,Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Stash...
因此,我们的目标是推断结构和模式,试图找到相似之处。 发现相似样本组(聚类 clustering),高维空间中数据的表示都属于无监督学习。 半监督学习(semi-supervised learning)算法使用标记和未标记数据的组合进行训练。通常,为了指导对无标记输入数据中存在的结构的研究,会使用数量有限的标记数据。 同样值得一提的是,强化学习...
: 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 ...
Cell clustering for spatial transcriptomics data with graph neural networks. Zenodo https://doi.org/10.5281/zenodo.6560643 (2022). Download references Acknowledgements This work was supported by grants from the National Natural Science Foundation of China (no. 61725302 to H.S.), 62073219 (to H.S...
machine-learning data-mining deep-learning clustering surveys representation-learning data-mining-algorithms network-embedding graph-convolutional-networks gcn graph-embedding graph-neural-networks self-supervised-learning deep-clustering graphclustering Updated Jan 5, 2025 Python shubho...
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. Graph clustering has the ...
代码链接:https://github.com/SAMPL-Weizmann/DeepCut 1. 摘要 图像分割是计算机视觉中的一个基本任务。为训练监督方法进行数据标注可能非常耗时费力,这促使了无监督方法的发展。当前的方法通常依赖于从预训练网络中提取深度特征来构建图,然后应用经典的聚类方法(如k均值和归一化割)作为后处理步骤。然而,这种方法将特征...
[arXiv 2022] Shift-Robust Node Classification via Graph Adversarial Clustering [paper] [arXiv 2021] CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks [paper] [arXiv 2021] Distributionally Robust Semi-Supervised Learning Over Graphs [paper] ...
(1) a "sweet spot" of relational graphs leads to neural networks with significantly improved predictive performance; (2) neural network's performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph; (3) our findings are consistent ...