Learning Effective Road Network Representation with Hierarchical Graph Neural Networks

2020 ◽  
Author(s):  
Ning Wu ◽  
Xin Wayne Zhao ◽  
Jingyuan Wang ◽  
Dayan Pan
Keyword(s):  
Neural Networks ◽  
Road Network ◽  
Network Representation ◽  
Hierarchical Graph ◽  
Graph Neural Networks

scholarly journals GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

2021 ◽  
Author(s):  
Ying Xia ◽  
Chun-Qiu Xia ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Neural Networks ◽  
Nucleic Acid ◽  
Biological Activities ◽  
New Drugs ◽  
Superior Performance ◽  
Nucleic Acid Binding ◽  
Hierarchical Graph ◽  
Binding Residue ◽  
Binding Residues ◽  
Graph Neural Networks

Abstract Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.

scholarly journals ComQA: Compositional Question Answering via Hierarchical Graph Neural Networks

2021 ◽  
Author(s):  
Bingning Wang ◽  
Ting Yao ◽  
Weipeng Chen ◽  
Jingfang Xu ◽  
Xiaochuan Wang
Keyword(s):  
Neural Networks ◽  
Question Answering ◽  
Hierarchical Graph ◽  
Graph Neural Networks

scholarly journals Extrapolating Paths with Graph Neural Networks

2019 ◽  
Author(s):  
Jean-Baptiste Cordonnier ◽  
Andreas Loukas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Road Network ◽  
Transportation Network ◽  
Shortest Path Problems ◽  
Graph Theoretic ◽  
Partially Observed ◽  
Gps Traces ◽  
Graph Neural Networks ◽  
Future Path

We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the interaction of an agent with a network---a driver on the transportation network, an information seeker in Wikipedia, or a client in an online shop. Our interest is sparked by the realization that, in contrast to shortest-path problems, natural paths are usually not optimal in any graph-theoretic sense, but might still follow predictable patterns. Our main contribution is a graph neural network called Gretel. Conditioned on a path prefix, this network can efficiently extrapolate path suffixes, evaluate path likelihood, and sample from the future path distribution. Our experiments with GPS traces on a road network and user-navigation paths in Wikipedia confirm that Gretel is able to adapt to graphs with very different properties, while also comparing favorably to previous solutions.

scholarly journals RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

2020 ◽  
Vol 34 (07) ◽  
pp. 10965-10972
Author(s):  
Songtao He ◽  
Favyen Bastani ◽  
Satvat Jagwani ◽  
Edward Park ◽  
Sofiane Abbar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Receptive Field ◽  
Spatial Correlation ◽  
Satellite Imagery ◽  
Road Network ◽  
Network Graph ◽  
The Road ◽  
Road Type ◽  
On The Road ◽  
Graph Neural Networks

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation – the limited effective receptive field of image classifiers.To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. Using a GNN allows information to propagate on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km2 area in 20 U.S. cities and a synthesized dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. In addition, RoadTagger is robust to disruptions in the satellite imagery and is able to learn complicated inductive rules for aggregating scattered information along the road network.

Hierarchical Graph Neural Networks for Few-Shot Learning

2021 ◽  
pp. 1-1
Author(s):  
Cen Chen ◽  
Kenli Li ◽  
Wei Wei ◽  
Joey Tianyi Zhou ◽  
Zeng Zeng
Keyword(s):  
Neural Networks ◽  
Hierarchical Graph ◽  
Graph Neural Networks
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