Results 41 to 50 of about 1,903,201 (339)
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs [PDF]
Workshop on Irregular Applications: Architectures and Algorithms, 2020 Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search.
Da Zheng +8 more
semanticscholar +1 more source
, 2023 In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs.
Zhang, Zhen +4 more
openaire +3 more sources
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
bioRxiv, 2020 Understanding how certain brain regions relate to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research.
Xiaoxiao Li +10 more
semanticscholar +1 more source
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations [PDF]
bioRxiv, 2021 Motivation Drug combination therapy has become a increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments ...
Jinxian Wang +4 more
semanticscholar +1 more source
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 +2 more
openaire +5 more sources
Online social network user performance prediction by graph neural networks
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN)
Fail Gafarov +2 more
doaj +1 more source
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 +3 more
semanticscholar +1 more source
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning [PDF]
International Journal of Production Research, 2021 We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the
Junyoung Park +4 more
semanticscholar +1 more source
Framework and Algorithms for Accelerating Training of Semi-supervised Graph Neural Network Based on Heuristic Coarsening Algorithms [PDF]
Jisuanji kexueGraph neural network is the mainstream tool of graph machine learning at the current stage,and it has broad development prospects.By constructing an abstract graph structure,the graph neural network model can be used to efficiently deal with problems in ...
CHEN Yufeng , HUANG Zengfeng
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