Graph convolutional networks: a comprehensive review
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TLDR
A comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models, is conducted and several open challenges are presented and potential directions for future research are discussed.Abstract:
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.read more
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Graph Convolutional Networks for Graphs Containing Missing Features
TL;DR: This approach integrates the processing of missing features and graph learning within the same neural network architecture and demonstrates through extensive experiments that this approach significantly outperforms the imputation based methods in node classification and link prediction tasks.
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Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues
TL;DR: A new taxonomy of ST-GNN is proposed by dividing existing models into four approaches such as graph convolutional recurrent neural network, fully graph Convolutional network, graph multi-attention network, and self-learning graph structure.
Journal ArticleDOI
Dynamic graph convolutional network for multi-video summarization
TL;DR: In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection.
Journal ArticleDOI
Graph Neural Network: A Comprehensive Review on Non-Euclidean Space
Nurul A. Asif,Yeahia Sarker,Ripon K. Chakrabortty,Michael J. Ryan,Md. Hafiz Ahamed,Dip Kumar Saha,Faisal R. Badal,Sajal K. Das,Md. Firoz Ali,Sumaya I. Moyeen,Md. Robiul Islam,Zinat Tasneem +11 more
TL;DR: Graph Neural Networks (GNNs) as mentioned in this paper provide a generalized form to exploit non-euclidean space data by exploiting the relationships among graph data, which can be visualized as an aggregation of nodes and edges without having any order.
Journal ArticleDOI
Graph neural networks for materials science and chemistry
Patrick Reiser,Marlen Neubert,André Eberhard,Luca Torresi,Chen Zhou,Chen Shao,Houssam Metni,Clint van Hoesel,Henrik Schopmans,Timo Sommer,Pascal Friederich +10 more
TL;DR: Graph Neural Networks (GNNs) as mentioned in this paper are one of the fastest growing classes of machine learning models and play an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials.
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