Who Cited It

Graph convolutional networks: a comprehensive review

2019 · Computational Social Networks · 1,877 citations · 3 from inside this corpus

Si Zhang, Hanghang Tong, Jiejun Xu low, Ross Maciejewski

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.

Graph convolutional networks: a comprehensive review (2019)Graph convolutional networks:…A Comprehensive Survey on Graph Neural Networks (2020)A Comprehensive Survey on Gra…The Graph Neural Network Model (2008)The Graph Neural Network ModelDeepWalk (2014)DeepWalkSemi-Supervised Classification with Graph Convolutional Networks (2016)Semi-Supervised Classificatio…A Comprehensive Survey on Graph Neural Networks (2020)A Comprehensive Survey on Gra…Graph Convolutional Neural Networks for Web-Scale Recommender Systems (2018)Graph Convolutional Neural Ne…Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning (2018)Deeper Insights Into Graph Co…A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2018)A Comprehensive Survey of Gra…Graph Convolutional Networks for Text Classification (2019)Graph Convolutional Networks …Graph embedding techniques, applications, and performance: A survey (2018)Graph embedding techniques, a…Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic\n Forecasting (2017)Diffusion Convolutional Recur…Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (2016)Convolutional Neural Networks…Graph based anomaly detection and description: a survey (2014)Graph based anomaly detection…Graph Neural Networks: A Review of Methods and Applications (2018)Graph Neural Networks: A Revi…Graph neural networks: A review of methods and applications (2020)Graph neural networks: A revi…Deep Learning on Graphs: A Survey (2020)Deep Learning on Graphs: A Su…Graph Neural Networks: A Review of Methods and Applications (2018)Graph Neural Networks: A Revi…
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What this paper cites, inside the corpus

PaperYearCited
A Comprehensive Survey on Graph Neural Networks20209,855
The Graph Neural Network Model20089,662
DeepWalk20148,670
Semi-Supervised Classification with Graph Convolutional Networks20168,058
A Comprehensive Survey on Graph Neural Networks20203,307
Graph Convolutional Neural Networks for Web-Scale Recommender Systems20182,836
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning20182,652
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications20182,076
Graph Convolutional Networks for Text Classification20192,000
Graph embedding techniques, applications, and performance: A survey20181,844
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic\n Forecasting20171,817
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering20161,703
Graph based anomaly detection and description: a survey20141,516
Graph Neural Networks: A Review of Methods and Applications20181,448

What cites it, inside the corpus

PaperYearCited
Graph neural networks: A review of methods and applications20205,808
Deep Learning on Graphs: A Survey20201,560
Graph Neural Networks: A Review of Methods and Applications20181,448

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Topics

Advanced Graph Neural NetworksComputer Science
Graph Theory and AlgorithmsComputer Science
Complex Network Analysis TechniquesPhysics and Astronomy

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  • supports4 author record(s) attached.
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