【摘要】 这篇论文是第一个提出Graph Neural Network模型的论文,它将神经网络使用在图结构数据上,并细述了神经网络模型了结构组成、计算方法、优化算法、流程实现等等。论文后面还对模型的复杂度进行了评估,以及在现实任务上进行了实验和比较(比较算法为NL、L、FNN)。 论文概要 这篇论文是第一个提出Graph Neural Net...
AliGraph: A Comprehensive Graph Neural Network Platform Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou VLDB 2019 Deep Graph Library DGL Team AmpliGraph Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof Euler Al...
3.7 Generation 3.8 Combinatorial Optimization 3.9 Adversarial Attack 3.10 Graph Clustering 3.11 Graph Classification 3.12 Reinforcement Learning 3.13 Traffic Network 3.14 Few-shot and Zero-shot Learning 3.15 Program Representation 3.16 Social Network 3.17 Graph Matching...
GraphINVENT is a platform for graph-based molecular generation using graph neural networks. GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time. All models implemented in GraphINVENT can quickly learn to build molecules resemblin...
They have demonstrated efficiency in a number of graph-based operations, including molecule generation, social network generation, and protein design. In addition, GGANs have shown potential in many areas by alternately creating nodes and edges and using adversarial training [55,56]. GGANs are ...
在图像识别和物体检测任务上的大量实验证明了我们的ViG架构的优越性。我们希望这项关于一般视觉任务的GNN开创性研究将为未来的研究提供有益的启发和经验。PyTorch代码可在https://github.com/huawei-noah/ Efficient-AI-Backbones和MindSpore代码可在此处获得https://gitee.com/mindspore/models。
of nucleotide-wise RBP binding profiles. We demonstrate its superior performance compared to GraphProt and two CNN-based methods on single as well as combined CLIP-seq datasets. Conceived as an end-to-end method, GraphProt2 includes all necessary functionalities, from dataset generation over model...
with them. In recent years, systems based on variants of graph neural networks such as graph convolutional network (GCN), graph attention network (GAT), gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a ...
153. In this framework, the generation process is decomposed into modular steps, e.g., whether to add a node or which nodes to connect. Each module is a fully-connected neural network modeling probabilities of executing particular types of modifications. You et al. suggested a purely RL-...
The generation of the adjacency matrix A in STAGATE depends on the dataset used. It can be generated by using either the KNN or the radius model. The KNN model was used for the data for 10x Visium, wherein the adjacency matrix comprised the six nearest neighbors. On the contrary, a rad...