Results 71 to 80 of about 748,129 (177)

GraphX-Net: A Graph Neural Network-Based Shapley Values for Predicting Breast Cancer Occurrence

IEEE Access
Breast cancer is a major health problem worldwide, and an accurate prediction of its recurrence is crucial to early detection of recurrence and personalized treatment.
Abdullah Basaad, Shadi Basurra, Edlira Vakaj, Mohammed Aleskandarany, Mohammed M. Abdelsamea   +4 more
doaj   +1 more source

Wide & Deep Learning for Node Classification

CoRR
Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its combination of the memorization ability of generalized linear models and the generalization ability of deep models.
Yancheng Chen, Wenguo Yang, Zhipeng Jiang   +2 more
openaire   +2 more sources

A Multi-Task Representation Learning Architecture for Enhanced Graph Classification

Frontiers in Neuroscience, 2020
Composed of nodes and edges, graph structured data are organized in the non-Euclidean geometric space and ubiquitous especially in chemical compounds, proteins, etc. They usually contain rich structure information, and how to effectively extract inherent
Yu Xie, Maoguo Gong, Yuan Gao, A. K. Qin, Xiaolong Fan   +4 more
doaj   +1 more source

Effective Temporal Graph Learning via Personalized PageRank

Entropy
Graph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible.
Ziyu Liao, Tao Liu, Yue He, Longlong Lin
doaj   +1 more source

Path Connectivity Based Neighbor‑Awareness Node Classification Algorithm

Shuju Caiji Yu Chuli
Graph convolutional neural networks obtain the node representation by aggregating the neighbor node information with high similarity，and selecting the appropriate neighborhood for the node and conducting effective aggregation are the keys to the graph ...
ZHENG Wenping, WANG Xiaomin, HAN Zhaorong   +2 more
doaj   +1 more source

Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms

CoRR, 2018
Embedding graph nodes into a vector space can allow the use of machine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs.
Kento Nozawa, Masanari Kimura, Atsunori Kanemura   +2 more
openaire   +2 more sources

Towards Classification of 5d SCFTs: Single Gauge Node

, 2017
We propose a number of apparently equivalent criteria necessary for the consistency of a 5d SCFT in its Coulomb phase and use these criteria to classify 5d SCFTs arising from a gauge theory with simple gauge group.
Jefferson, Patrick, Kim, Hee-Cheol, Vafa, Cumrun, Zafrir, Gabi   +3 more
core  

Reputation-Based Leader Selection Consensus Algorithm with Rewards for Blockchain Technology

Computers
Blockchain technology is an emerging decentralized and distributed technology that can maintain data security. It has the potential to transform many sectors completely.
Munir Hussain, Amjad Mehmood, Muhammad Altaf Khan, Rabia Khan, Jaime Lloret   +4 more
doaj   +1 more source

AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks

IEEE Access
Graph neural networks (GNNs), particularly graph convolutional networks (GCNs), have demonstrated remarkable success in modeling graph-structured data across diverse applications.
Chuan Dai, Yajuan Wei, Hao Wang, Ying Liu, Zhijie Xu   +4 more
doaj   +1 more source

Eigenvector Distance-Modulated Graph Neural Network: Spectral Weighting for Enhanced Node Classification

Mathematics
Graph Neural Networks (GNNs) face significant challenges in node classification across diverse graph structures. Traditional message passing mechanisms often fail to adaptively weight node relationships, thereby limiting performance in both homophilic ...
Ahmed Begga, Francisco Escolano, Miguel Ángel Lozano   +2 more
doaj   +1 more source