Results 51 to 60 of about 1,639,507 (316)

Global Context Enhanced Graph Neural Networks for Session-based Recommendation [PDF]

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020
Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the ...
Ziyang Wang, Wei Wei, G. Cong, Xiaoli Li, Xian-Ling Mao, Minghui Qiu   +5 more
semanticscholar   +1 more source

k-hop graph neural networks

Neural Networks, 2020
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in terms of distinguishing non-isomorphic graphs.
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis   +2 more
openaire   +5 more sources

MBHAN: Motif-Based Heterogeneous Graph Attention Network

Applied Sciences, 2022
Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu, Weiping Lin, Minli Tang, Jiatao Jiang   +3 more
doaj   +1 more source

Graph Rewriting for Graph Neural Networks

, 2023
Originally submitted to ICGT 2023, part of STAF ...
Machowczyk, Adam, Heckel, Reiko
openaire   +2 more sources

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, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang   +4 more
doaj   +1 more source

Review of Graph Neural Networks [PDF]

Jisuanji kexue
With the rapid development of artificial intelligence,deep learning has achieved great success in data that can be represented in Euclidean spaces,such as images,text,and speech.However,it has been difficult to apply deep learning to non-Eucli-dean ...
HOU Lei, LIU Jinhuan, YU Xu, DU Junwei
doaj   +1 more source

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Data Science and Engineering, 2023
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li, Man-Sheng Chen, Yuefang Gao, Chang-Dong Wang   +3 more
doaj   +1 more source

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
openaire   +3 more sources

Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

Frontiers in Artificial Intelligence, 2021
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel.
Paul Y. Wang, Paul Y. Wang, Sandalika Sapra, Sandalika Sapra, Vivek Kurien George, Vivek Kurien George, Gabriel A. Silva, Gabriel A. Silva, Gabriel A. Silva   +8 more
doaj   +1 more source

Benchmarking Graph Neural Networks

, 2020
Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Dwivedi, Vijay Prakash, Joshi, Chaitanya K., Luu, Anh Tuan, Laurent, Thomas, Bengio, Yoshua, Bresson, Xavier   +5 more
openaire   +3 more sources