Results 1 to 10 of about 7,352,426 (296)

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, Qingsong Li, Wei Wei, Zhiming Zheng   +3 more
doaj   +1 more source

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power
Giorgos Bouritsas, Fabrizio Frasca, Stefanos P. Zafeiriou, M. Bronstein   +3 more
semanticscholar   +1 more source

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2022
Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider both complex intra-variable dependencies and inter-variable dependencies ...
Ling Chen, Donghui Chen, Zongjiang Shang, Binqing Wu, Cen Zheng, Bo Wen, Wei Zhang   +6 more
semanticscholar   +1 more source

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, Dong-Jin Song, Chao Huang, A. Swami, N. Chawla   +4 more
semanticscholar   +1 more source

MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

Chemical Science, 2022
Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction.
Ziduo Yang, Weihe Zhong, Lu Zhao, Calvin Yu-Chian Chen   +3 more
semanticscholar   +1 more source

Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node 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
doaj   +1 more source

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction [PDF]

International Conference on Information and Knowledge Management, 2022
The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists.
Sheng Xiang, Dawei Cheng, Chencheng Shang, Ying Zhang, Yu-Qi Liang   +4 more
semanticscholar   +1 more source

Network Slicing End-to-end Latency Prediction Based on Heterogeneous Graph Neural Network [PDF]

Jisuanji kexue
End-to-end latency,as a crucial performance metric for network slicing,is difficult to predict accurately via modeling due to the influences of network topology,traffic model,and scheduling policies.To tackle the above issues,we propose a heterogeneous ...
HU Haifeng, ZHU Yiwen, ZHAO Haitao
doaj   +1 more source

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

The Web Conference, 2020
Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation ...
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Ji-Liang Tang, Caiyan Jia, Jian Yu   +7 more
semanticscholar   +1 more source

A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

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
doaj   +1 more source