Results 111 to 120 of about 6,341,676 (193)

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification

IEEE Access
Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising ...
Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed   +4 more
doaj   +1 more source

Explaining the structure of inter-organizational networks using exponential random graph models: does proximity matter? [PDF]

A key question raised in recent years is which factors determine the structure of inter-organizational networks. While the focus has primarily been on different forms of proximity between organizations, which are determinants at the dyad level, recently ...
Tom Broekel, Matte Hartog
core  

Graph-based vision transformer with sparsity for training on small datasets from scratch

Scientific Reports
Vision Transformers (ViTs) have achieved impressive results in large-scale image classification. However, when training from scratch on small datasets, there is still a significant performance gap between ViTs and Convolutional Neural Networks (CNNs ...
Peng Li, Lu Huang, Jin Li, Haiyan Yan, Dongjing Shan   +4 more
doaj   +1 more source

LISA: language independent sentiment analysis using graph neural networks

Complex & Intelligent Systems
This paper presents LISA as a Language Independent Sentiment Analysis tool that exploits Graph Neural Networks, GNN, for sentiment analysis, SA, applications. To build up that analyzer two types of GNN are examined.
Mohamed Zaki, Basheer Youssef, Salwa El-gamal, Mohamed Abd-Elrahem   +3 more
doaj   +1 more source

Anomaly detection in graph databases using graph neural networks: Identifying unusual patterns in graphs

Egyptian Informatics Journal
Anomaly detection in graph-structured data is a critical task in various applications, including social networks, fraud detection, and educational platforms.
Ismail Chetoui, Essaid El Bachari, Mohamed El Adnani, Mohamed Ouhssini   +3 more
doaj   +1 more source

Addressing imbalance in graph datasets: Introducing GATE-GNN with graph ensemble weight attention and transfer learning for enhanced node classification

Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven data. Specifically in signed and weighted graph structures. This makes classification tasks less effective.
Fofanah, Abdul Joseph, Chen, David, Zhang, Shaoyang, Wen, Lian   +3 more
core   +1 more source

Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks [PDF]

Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems.
Deng, Lei, Pei, Jing, Yin, Huifeng, Xu, Mingkun   +3 more
core  

Graph Convolutional Networks and Graph Attention Networks for Approximating Arguments Acceptability

International audienceVarious approaches have been proposed for providing efficient computational approaches for abstract argumentation. Among them, neural networks have permitted to solve various decision problems, notably related to arguments ...
Mailly, Jean-Guy, Cibier, Paul
core   +1 more source

A microbe-drug association prediction model based on graph attention networks and rotation forest. [PDF]

Front Bioinform
Li J, Li J, Chen Q, Wang Z, Liu X, Liang M, Wang J, Ding H, Zeng B, Wang L.   +9 more
europepmc   +1 more source

Reverse Engineering of Binary Programs using Graph Attention Networks

Understanding the functionality and behavior of binary code is essential for many software engineering tasks, including malware analysis, vulnerability detection, and program optimization.
Kanigiri, Sai Nikhila
core   +1 more source