Results 51 to 60 of about 1,903,201 (339)
Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities
Mathematics, 2022 Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start
Xing Li +3 more
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Heterogeneous Graph Neural Network
Knowledge Discovery and Data Mining, 2019 Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however,
Chuxu Zhang +4 more
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ChatGPT Informed Graph Neural Network for Stock Movement Prediction [PDF]
Social Science Research Network, 2023 ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored ...
Zihan Chen +4 more
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Non-Local Graph Neural Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 8 pages, 2 figures, accepted by ...
Meng Liu, Zhengyang Wang, Shuiwang Ji
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Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [PDF]
Knowledge Discovery and Data Mining, 2021 Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge ...
Chuizheng Meng +2 more
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Benchmarking Graph Neural Networks
, 2020 Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Dwivedi, Vijay Prakash +5 more
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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Federated Social Recommendation with Graph Neural Network [PDF]
ACM Transactions on Intelligent Systems and Technology, 2021 Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to
Zhiwei Liu +4 more
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Graph Convolutional Neural Network [PDF]
, 2016 The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the image classification.
Xianghua Xie
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IEEE Access, 2023 To improve the accuracy of graph neural network recommendation algorithms, research mainly integrates multi head attention mechanism and GRU, which is to construct a graph neural network recommendation model; Considering the long and short term ...
Fang Liu, Juan Wang, Junye Yang
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