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Article
Computer Science, Artificial Intelligence
Ziwei Zhang et al.
Summary: This survey comprehensively reviews the application of deep learning methods on graph data. The existing methods are categorized into five types, and their development history, differences, and compositions are covered in a systematic manner. Potential future research directions are also discussed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Yu Xie et al.
Summary: Researchers propose a novel active and semi-supervised graph neural network framework, which can effectively perform graph classification tasks using a small number of labeled and unlabeled graph examples, and achieves competitive performance on benchmark graph datasets.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Artificial Intelligence
Yaochen Xie et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Min Shi et al.
Summary: In this article, the authors propose a novel multi-label graph convolutional network (MuLGCN) for learning node representation in complex networks. They model a multi-label network as two Siamese GCNs and integrate them in one objective function to explore label-label correlation and network topology structures. Experiments validate the effectiveness of the proposed approach in multi-label node classification.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Proceedings Paper
Computer Science, Information Systems
Xueying Yang et al.
Summary: This research investigates automated graph neural network calibration by incorporating uncertainty estimation into hyperparameter optimization problem, proposing a hyperparameter uncertainty-induced graph convolutional network with promising results. Experimental results demonstrate the effectiveness of the proposed method in terms of node classification accuracy and expected calibration error compared to several state-of-the-art uncertainty-aware and calibrated GCN methods.
PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022
(2022)
Article
Computer Science, Information Systems
Yiding Zhang et al.
Summary: This article discusses how to use a graph attention network to learn node representations in hyperbolic spaces. By utilizing the gyrovector space framework and the proximity in the product of hyperbolic spaces, an improved model is proposed and its effectiveness is demonstrated through comprehensive experiments.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Artificial Intelligence
Zonghan Wu et al.
Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Indro Spinelli et al.
Summary: This brief introduces an adaptive propagation GCN model that achieves superior or similar results to the best proposed models by adjusting the number of communication steps independently at every node. Additionally, a regularization term is investigated to enforce an explicit tradeoff between communication and accuracy. The code for the AP-GCN experiments has been released as an open-source library.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yiding Zhang et al.
Summary: This paper introduces a novel hyperbolic GCN called Lorentzian graph convolutional network (LGCN), which rigorously guarantees the learned node features follow the hyperbolic geometry. Experimental results show that LGCN outperforms existing methods, especially in learning the representation of tree-like graphs.
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Ronghang Zhu et al.
Summary: Due to the remarkable capability of extracting effective graph embeddings, GCN and its variants have been successfully applied to various tasks. However, training GCN models is challenging, especially for deep models, as they are sensitive to hyperparameters. This paper proposes a self-tuning GCN approach with an alternate training algorithm and population based training scheme to automate the training process and achieve better optimization results.
SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Bo Jiang et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
(2019)
Proceedings Paper
Computer Science, Information Systems
Wei-Lin Chiang et al.
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Zaiqiao Meng et al.
PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19)
(2019)