Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification

Zhuang, Chenyi; Ma, Qiang

doi:10.1145/3178876.3186116

Cited by 319 publications

(191 citation statements)

References 14 publications

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“…Graph-to-sequence learning learns to generate sentences with the same meaning given a semantic graph of abstract [22], [23], [25], [41], [43], [44], [45] [49], [50], [51], [53], [56], [61], [62] Citeseer [117] 1 3327 4732 3703 6 [22], [41], [43], [45], [50], [51], [53] [56], [61], [62] Pubmed [117] 1 19717 44338 500 3 [18], [22], [25], [41], [43], [44], [45] [49], [51], [53], [55], [56], [61], [62] [70], [95] DBLP (v11) [118] 1 4107340 36624464 -- [64], [70], [99] Biochemical Graphs PPI [119] 24 56944 818716 50 121 [18], [42], [43], [48], [45], [50]...…”

Section: Practical Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Graph Neural Networks

Pan

Chen

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

6,146

2,977

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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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Section: Practical Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Graph Neural Networks

Pan

Chen

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

6,146

2,977

View full text Add to dashboard Cite

show abstract

“…ManiReg [1] 59.5 SemiEmb [49] 59.0 LP [57] 68.0 DeepWalk [32] 67.2 ICA [29] 75.1 Planetoid* [50] 75.7 Graph-CNN [15] 81.5 MoNet [30] 81.7 GAT [41] 83.0 LGCN [8] 83.3 Dual GCN [59] 83.5 ACNet 83.5 The role of global inference. We first evaluate the effectiveness of global inference by constructing two different networks, i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, several limited attempts [15,38,8,59] have been made to extend CNNs for handling graph data. For instance, Kipf et al [15] presented a layer-wise propagation rule for CNNs to operate directly on graph-structured data.…”

Section: Related Workmentioning

confidence: 99%

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Adaptively Connected Neural Networks

Wang¹,

Wang

Li³

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) in two aspects. First, ACNet employs a flexible way to switch global and local inference in processing the internal feature representations by adaptively determining the connection status among the feature nodes (e.g., pixels of the feature maps) 1 . We can show that existing CNNs, the classical multilayer perceptron (MLP), and the recently proposed non-local network (NLN) [48] are all special cases of ACNet. Second, ACNet is also capable of handling non-Euclidean data. Extensive experimental analyses on a variety of benchmarks (i.e., ImageNet-1k classification, COCO 2017 detection and segmentation, CUHK03 person re-identification, CIFAR analysis, and Cora document categorization) demonstrate that ACNet cannot only achieve state-of-the-art performance but also overcome the limitation of the conventional MLP and CNN 2 . The code is available at https://github.com/wanggrun/ Adaptively-Connected-Neural-Networks.

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“…[35] proposes a dual-model paradigm to merge the training of two dual tasks. [45] extends GCN to dual structures, combining global and local consistencies in graph. [2] proposes DELF conducting dual embedding for users and items in recommender systems.…”

Section: Related Workmentioning

confidence: 99%

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Zhang

Gao

et al. 2019

The World Wide Web Conference

267

152

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Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative ltering methods. However, most existing models assume that social e ects from friend users are static and under the forms of constant weights or xed constraints. To relax this strong assumption, in this paper, we propose dual graph a ention networks to collaboratively learn representations for two-fold social e ects, where one is modeled by a user-speci c a ention weight and the other is modeled by a dynamic and context-aware a ention weight. We also extend the social e ects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that di erent social e ects in two domains could interact with each other and jointly inuence users' preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social e ects. Experiments on one benchmark dataset and a commercial dataset verify the e cacy of the key components in our model. e results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

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Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification

Cited by 319 publications

References 14 publications

A Comprehensive Survey on Graph Neural Networks

A Comprehensive Survey on Graph Neural Networks

Adaptively Connected Neural Networks

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

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