Zonghan Wu, Shirui Pan, Fengwen Chen
Guodong Long, Chengqi Zhang, Philip S. Yu
2019 · arXiv
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
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A survey paper is usually a good way to go, such as A comprehensive survey on graph neural networks (https://arxiv.org/abs/1901.00596) or Graph Neural Networks: A Review of Methods and Applications (https://arxiv.org/abs/1812.08434)
On the contrary, Graph NN's I've found Graph NNs extraordinarily useful and very easy to train! I'm not sure what kind of problems you are trying to use them for, but generally they are really useful for node classification or recommendation type tasks. So for example in the knowledge graph context, you can do things like give it a tiger, jaguar and panther and it will find nodes like lions and leopards. https://arxiv.org/pdf/1901.00596.pdf is a survey paper which has a decent overview of the tasks they are useful for. http://research.baidu.com/Public/uploads/5c1c9a58317b3.pdf is using them for QA, but I'm not familiar enough with the dataset to evaluate it fairly.
> At which NLP tasks are they state the art? The only one where they are really competitive is dependency parsing. (from my own disjonction of cases) At tasks that actually involve graphs presumably. https://arxiv.org/pdf/1901.00596.pdf https://paperswithcode.com/task/graph-classification has a bunch of GNNs ranked #1 > Also were there any new real SOTA on any NLP tasks since last summer? I feel like accuracy progress has frozen.. That's pretty normal for winter/spring, it's not conference season.
Graph network reviews seem all the rage, here's another recent one: "A Comprehensive Survey on Graph Neural Networks " https://arxiv.org/abs/1901.00596
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