Deep Learning on Graphs: A Survey

  title={Deep Learning on Graphs: A Survey},
  author={Ziwei Zhang and Peng Cui and Wenwu Zhu},
  journal={IEEE Transactions on Knowledge and Data Engineering},
Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep… 

Deep Graph Generators: A Survey

A comprehensive survey on deep learning-based graph generation approaches is conducted and classifies them into five broad categories, namely, autoregressive, autoencoder-based, reinforcement learning- based, adversarial, and flow- based graph generators, providing the readers a detailed description of the methods in each class.

Graph Deep Learning: State of the Art and Challenges

A review of the state of graph representation learning from the perspective of deep learning and identifies four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models.

Survey of Graph Neural Networks and Applications

The artificial neural networks and GNNs are reviewed, ways to extend deep learning models to deal with datasets in non-Euclidean space are presented, and the GNN-based approaches based on spectral and spatial strategies are introduced.

Graph Neural Network and its applications

An overview of the current research status of graph neural networks and proposed improved algorithms to further promote breakthroughs in more applications of GNNs are provided.

A Survey on Deep Graph Generation: Methods and Applications

A comprehensive review on the existing literature of graph generation from a variety of emerging methods to its wide application areas and divides the state-of-the-art methods into three categories based on model architectures and summarizes their generation strategies.

Graph Neural Networks: Self-supervised Learning

This chapter summarizes recent developments in applying SSL to GNNs categorizing them via the different training strategies and types of data used to construct their pretext tasks, and discusses open challenges for future directions.

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

This paper surveys and categorizes existing GNN models into the spatial and spectral domains, and reveals connections among subcategories in each domain, and proposes a unified framework that can widely fitexisting GNNs into this framework methodologically.

Geometric machine learning: research and applications

A comprehensive overview of geometric deep learning and comparative methods is provided, highlighting the applications and benchmark datasets of these methods across various research domains and proposing potential research directions and challenges.

Graph Learning: A Survey

A comprehensive overview on the state of the art of graph learning is presented, including graph signal processing, matrix factorization, random walk, and deep learning, and major models and algorithms under these categories are reviewed respectively.



Graph Convolutional Networks: Algorithms, Applications and Open Challenges

A comprehensive review of the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models, and introduces two taxonomies to group the existing works based on the types of convolutions and the areas of applications.

Deep Convolutional Networks on Graph-Structured Data

This paper develops an extension of Spectral Networks which incorporates a Graph Estimation procedure, that is test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate.

Adversarial Attacks on Neural Networks for Graph Data

This work introduces the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions, and generates adversarial perturbations targeting the node's features and the graph structure, taking the dependencies between instances in account.

Adversarial Attacks on Graph Neural Networks via Meta Learning

The core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks on graph neural networks for node classification that perturb the discrete graph structure, essentially treating the graph as a hyperparameter to optimize.

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

This paper proposes a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features and test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches.

Adversarial Attack on Graph Structured Data

This paper proposes a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier, and uses both synthetic and real-world data to show that a family of Graph Neural Network models are vulnerable to adversarial attacks.

Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

It is shown that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers.

Hierarchical Graph Representation Learning with Differentiable Pooling

DiffPool is proposed, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion.

An End-to-End Deep Learning Architecture for Graph Classification

This paper designs a localized graph convolution model and shows its connection with two graph kernels, and designs a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs.

Adversarial Attack and Defense on Graph Data: A Survey

This work systemically organize the considered works based on the features of each topic and provides a unified formulation for adversarialLearning on graph data which covers most adversarial learning studies on graph.