Results 61 to 70 of about 6,341,676 (193)

Contrastive Graph Similarity Networks

, 2023
Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a variety of fields, including object tracking, recommender systems, similarity search, and so on.
Li, Fuyi, Jin, D., Zheng, Yizhen, Jin, Di, Li, F., Pan, S., Pan, Shirui, Qiao, Yongliang, Zheng, Y., Wang, Luzhi, Qiao, Y., Wang, L., Yongliang Qiao, Fuyi Li, Shirui Pan, Di Jin, Luzhi Wang, Yizhen Zheng   +17 more
core   +1 more source

Learnable Graph Convolutional Attention Networks

CoRR, 2022
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures, respectively, GCNs and GATs.
Adrián Javaloy, Pablo Sánchez-Martín, Amit Levi 0001, Isabel Valera   +3 more
openaire   +4 more sources

A study of Graph Neural Networks and Graph Attention Networks for Node Classification [PDF]

Graph representations have received considerable attention as they can capture complex data relationships represented as nodes and edges, such as molecular structures, social network analysis, healthcare, and citation networks, etc. Due to this diversity
Bhargava, Yash
core   +2 more sources

Multi-Agent Transformer Networks With Graph Attention

IEEE Access
In addressing multi-agent reinforcement learning (MARL) challenges, Multi-Agent Transformer (MAT) has demonstrated a number of notable successes. In various benchmarks, MAT consistently showed a strong performance.
Woobeen Jin, Hyukjoon Lee
doaj   +1 more source

A Self-Attention Augmented Graph Convolutional Clustering Networks for Skeleton-Based Video Anomaly Behavior Detection

Applied Sciences, 2021
In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. The skeleton data have been proved to be robust to the complex background, illumination changes, and ...
Chengming Liu, Ronghua Fu, Yinghao Li, Yufei Gao, Lei Shi, Weiwei Li   +5 more
doaj   +1 more source

Prototype-Guided Attention for Graph Neural Networks

Applied Sciences
The graph neural network (GNN) has demonstrated strong performance in modelling graph-structured data across multiple application domains. However, existing GNN models do not fully exploit the information inherent in data.
Yiran Sun, Hak-Keung Lam
doaj   +1 more source

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

, 2022
Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on ...
Zou, Junni, Xu, Mingxing, Xiong, Hongkai, Dai, Wenrui, Frossard, Pascal, Li, Chenglin, Mingxing Xu, Pascal Frossard, Junni Zou, Wenrui Dai, Hongkai Xiong, Chenglin Li   +11 more
core   +1 more source

Context-Aware Graph Attention Networks

CoRR, 2019
Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation.
Bo Jiang 0002, Leiling Wang, Jin Tang 0001, Bin Luo 0001   +3 more
openaire   +2 more sources

Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

Machine Learning with Applications
In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes.
Parisa Foroutan, Salim Lahmiri
doaj   +1 more source

Enhancing Graph Summarization Using Node Importance and Graph Attention Networks

Mathematics
As the scale of graph-structured data continues to grow, graph summarization has become an important technique for storage efficiency and high-level visualization.
Krista Rizman Žalik, Domen Mongus, Mitja Žalik   +2 more
doaj   +1 more source