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Machine Learning on Graphs: A Model and Comprehensive Taxonomy
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TLDR
A comprehensive taxonomy of representation learning methods for graph-structured data is proposed, aiming to unify several disparate bodies of work and provide a solid foundation for understanding the intuition behind these methods, and enables future research in the area.Abstract:
There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categories, based on the availability of labeled data. The first, network embedding (such as shallow graph embedding or graph auto-encoders), focuses on learning unsupervised representations of relational structure. The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning. The third, graph neural networks, aims to learn differentiable functions over discrete topologies with arbitrary structure. However, despite the popularity of these areas there has been surprisingly little work on unifying the three paradigms. Here, we aim to bridge the gap between graph neural networks, network embedding and graph regularization models. We propose a comprehensive taxonomy of representation learning methods for graph-structured data, aiming to unify several disparate bodies of work. Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which generalizes popular algorithms for semi-supervised learning on graphs (e.g. GraphSage, Graph Convolutional Networks, Graph Attention Networks), and unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc) into a single consistent approach. To illustrate the generality of this approach, we fit over thirty existing methods into this framework. We believe that this unifying view both provides a solid foundation for understanding the intuition behind these methods, and enables future research in the area.read more
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Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TL;DR: A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.
Journal ArticleDOI
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TL;DR: In this paper, the authors propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.
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Benchmarking Graph Neural Networks
TL;DR: A reproducible GNN benchmarking framework is introduced, with the facility for researchers to add new models conveniently for arbitrary datasets, and a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs).
Journal Article
Benchmarking Graph Neural Networks
TL;DR: A reproducible GNN benchmarking framework is introduced, with the facility for researchers to add new models conveniently for arbitrary datasets, and a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs).
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TUDataset: A collection of benchmark datasets for learning with graphs.
Christopher Morris,Nils M. Kriege,Franka Bause,Kristian Kersting,Petra Mutzel,Marion Neumann +5 more
TL;DR: The TUDataset for graph classification and regression is introduced, which consists of over 120 datasets of varying sizes from a wide range of applications and provides Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
References
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Neural Embeddings of Graphs in Hyperbolic Space
TL;DR: A new concept that exploits recent insights and proposes learning neural embeddings of graphs in hyperbolic space is presented and experimental evidence that embedding graphs in their natural geometry significantly improves performance on downstream tasks for several real-world public datasets is provided.
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NetLSD: Hearing the Shape of a Graph
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Network mapping by replaying hyperbolic growth
TL;DR: HyperMap as discussed by the authors is a simple method to map a real network to its hyperbolic space by replays the network's geometric growth, estimating at each time-step the hyper-bolic coordinates of new nodes in a growing network by maximizing the likelihood of the network snapshot in the model.
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Graphite: Iterative Generative Modeling of Graphs
TL;DR: This work proposes Graphite, an algorithmic framework for unsupervised learning of representations over nodes in large graphs using deep latent variable generative models, parameterizes variational autoencoders (VAE) with graph neural networks, and uses a novel iterative graph refinement strategy inspired by low-rank approximations for decoding.
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On the equivalence between graph isomorphism testing and function approximation with GNNs
TL;DR: It is proved that order-2 Graph G-invariant networks fail to distinguish non-isomorphic regular graphs with the same degree, and is extended to a new architecture, Ring-GNNs, which succeeds on distinguishing these graphs and provides improvements on real-world social network datasets.