Graph Wavelet Neural Network Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng ICLR 2019 Supervised Community Detection with Line Graph Neural Networks Zhengdao Chen, Xiang Li, Joan Bruna ICLR 2019 Predict then Propagate: Graph Neural Networks meet Personalized PageRank Johannes Klicpera, ...
- Graph level task: In a graph-level task, our goal is to predict the property of an entire graph. For example, for a molecule represented as a graph, we might want to predict what the molecule smells like, or whether it will bind to a receptor implicated in a disease. - Node-lev...
To address above challenges, we propose a novel Curvature-based Adaptive Graph Neural Network (CurvAGN) for predicting protein-ligand binding affinity. The CurvAGN comprises a curvature block and an adaptive attention guided neural block (AGN). The curvature block assigns edge attributes to include ...
For evaluating the computational efficiency of our GNN model with respect to CNN models, three different 3D CNN models that have previously been utilized to predict the properties of polycrystalline12or two-phase8,29composite microstructures were trained separately under the same hardware (one Tesla P1...
1.17M 文档页数: 8页 顶/踩数: 0/0 收藏人数: 0 评论次数: 0 文档热度: 文档分类: 待分类 系统标签: graphinteractionsstructuredneuralentitypredicting MR-GNN:Multi-ResolutionandDualGraphNeuralNetworkforPredictingStructuredEntityInteractionsNuoXu1,PinghuiWang2,1∗†,LongChen1,JingTao1andJunzhouZhao1∗...
Scalable Graph Embedding for Asymmetric Proximity Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao AAAI 2017 Fast Network Embedding Enhancement via High Order Proximity Approximation Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu ...
for computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction framework based on graph convolutional neural networks (GCNs). In contrast to current CNN methods, GraphProt2 offers native support for the encoding of base pair ...
Link prediction is to predict whether two nodes in a network are likely to have a link. Related works 此前的做链路预测的方法主要是使用评分函数去测度链路存在的可能性的启发式算法 e.g. common neighbors、Katz index 缺陷 对链路存在的情境有很强的假设,当假设不成立时有效性受限。比如:common neighbors...
Hence, designing a more effective deep learning method for predicting DTA remains attractive. Results Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA ...
Inspired by the GN framework, we first construct a topological graph of the road network and feed the whole graph into the neural network and obtain a graph as output. Moreover, we combine the LSTM and GN block to build a new model to predict the traffic speed, where an encoder-decoder...