Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all ...
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the...
In this work, we ask if it is possible to significantly reduce the graph size while providing sufficient information to well train GNN models. 在这项工作中,我们发问是否有可能显着减少图的大小,同时提供足够的信息来很好地训练 GNN 模型。 Motivated by dataset distillation (Wang et al., 2018) and...
Graph Neural Networks LabML. https://nn.labml.ai/graphs/index.html (2023).7.LaBonne, M. Graph Attention Networks: Theoretical and Practical Insights https : / / mlabonne . github.io/blog/posts/2022-03-09-graph_attention_net...
通用 图生成 GraphRNN 《GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models》 通用 图生成 MolGAN 《 Molgan: An implicit generative model for small molecular graphs》 决策优化 旅行商问题 GNN 《Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP》《Att...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 3. Applications 3.1 Physics 3.2 Chemistry and Biology 3.3 ...
In fact, before the rise of deep learning, the industry has already begun to explore the technology of Graph Embedding[1]. The early graph embedding algorithms were mostly based on heuristic matrix decomposition and probabilistic graph models; later, more "shallow" neural network models represented...
1.DGL Team. 9 Graph Attention Network (GAT) Deep Graph Library (DGL). https: //docs .dgl.ai/ en/0.8.x/tutorials/models/1_gnn/9_gat.html (2023). 2.Graph Attention Networks LabML. https://nn.labml.ai/graphs/gat/index.html (2023). ...
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang WSDM 2018 Learning Structural Node Embeddings via Diffusion Wavelets Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec ...
Song, W., Yin, H., Liu, C., Song, D.: DeepMem: learning graph neural network models for fast and robust memory forensic analysis. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 606–618 (2018) Google Scholar Sun, X., Dai, J., Liu...