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A Comprehensive Survey on Graph Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 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.
Zonghan Wu, Shirui Pan, Guodong Long
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Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
IEEE Access, 2021 Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also ...
Joakim Skarding +2 more
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Schatten Graph Neural Networks
IEEE Access, 2022 Graph Neural Networks (GNNs) have been intensively studied in recent years because of their promising performance over graph-structural data and have provided assistance in many fields.
Youfa Liu +3 more
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A Review of Graph Neural Networks and Their Applications in Power Systems
Journal of Modern Power Systems and Clean Energy, 2022 Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains.
Wenlong Liao +4 more
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Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
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Survey of Graph Neural Network [PDF]
Jisuanji gongcheng, 2021 With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
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Mathematical Expressiveness of Graph Neural Networks
Mathematics, 2022 Graph Neural Networks (GNNs) are neural networks designed for processing graph data. There has been a lot of focus on recent developments of graph neural networks concerning the theoretical properties of the models, in particular with respect to their ...
Guillaume Lachaud +2 more
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Prototype-based Interpretable Graph Neural Networks [PDF]
, 2022 Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks.
Biagio La Rosa +2 more
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Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks
Machine Learning and Knowledge Extraction, 2022 Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications.
Youfa Liu, Guo Chen
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Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [PDF]
Jisuanji kexue, 2022 With the wide application of graph neural network technology in the field of natural language processing,the research of text classification based on graph neural networks has received more and more attention.Building graph for text is an important ...
ZHANG Hu, BAI Ping
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