Results 11 to 20 of about 1,708,308 (346)
Binarized graph neural network [PDF]
World Wide Web, 2021 Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based ...
Hanchen Wang 0001 +6 more
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Curvature graph neural network [PDF]
Information Sciences, 2022 Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism.
Haifeng Li 0007 +5 more
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Atomistic Line Graph Neural Network for improved materials property predictions [PDF]
npj Computational Materials, 2021 Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.
K. Choudhary, Brian L. DeCost
semanticscholar +1 more source
Survey of Graph Neural Network in Recommendation System [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well.
WU Jing, XIE Hui, JIANG Huowen
doaj +1 more source
Graph Neural Network for Traffic Forecasting: A Survey [PDF]
Expert systems with applications, 2021 Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model ...
Weiwei Jiang, Jiayun Luo
semanticscholar +1 more source
Study on Degree of Node Based Personalized Propagation of Neural Predictions forSocial Networks [PDF]
Jisuanji kexue, 2023 Graph is an important and fundamental data structure that presents in a wide variety of practical scenarios.With the rapid development of the Internet in recent years,there has been a huge increase in social network graph data,and the analysis of this ...
SHAO Yunfei, SONG You, WANG Baohui
doaj +1 more source
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning [PDF]
Knowledge Discovery and Data Mining, 2021 Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN).
Xiao Wang, Nian Liu, Hui-jun Han, C. Shi
semanticscholar +1 more source
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding [PDF]
The Web Conference, 2020 A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous graph into low ...
Xinyu Fu +3 more
semanticscholar +1 more source
Network In Graph Neural Network
CoRR, 2021 Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and knowledge graph reasoning. In this regard, various strategies have been proposed in the past to improve the expressiveness of GNNs.
Xiang Song 0003 +4 more
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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [PDF]
Knowledge Discovery and Data Mining, 2020 Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph ...
J. Qiu +7 more
semanticscholar +1 more source