Results 61 to 70 of about 1,708,308 (346)

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

Graph Condensation for Graph Neural Networks

CoRR, 2021
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original graph into a small, synthetic and
Wei Jin 0009, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah   +5 more
openaire   +3 more sources

CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection [PDF]

, 2023
Graph neural architecture search (GNAS) has been successful in many supervised learning tasks, such as node classification, graph classification, and link prediction.
Raeed Al-Sabri, Zhang, Ji, Li, Zhao, Oloulade, Babatounde Moctard, Babatounde Moctard Oloulade, Gao, Jianliang, Chen, Jiamin, Ji Zhang, Jiamin Chen, Zhao Li, Lyu, Tengfei, Al-Sabri, Raeed, Tengfei Lyu, Jianliang Gao   +13 more
core   +1 more source

Benchmarking Graph Neural Networks

J. Mach. Learn. Res., 2020
Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent 0001, Yoshua Bengio, Xavier Bresson   +5 more
openaire   +4 more sources

Graph Summarization with Graph Neural Networks

CoRR, 2022
The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge labels. Based on these neighborhood characteristics, the vertex is assigned to an equivalence class.
Maximilian Blasi, Manuel Freudenreich, Johannes Horvath, David Richerby, Ansgar Scherp   +4 more
openaire   +2 more sources

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

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, Yuqi Liang   +4 more
semanticscholar   +1 more source

A Graph Neural Networks approach to the state estimation of water distribution systems [PDF]

, 2023
openTraditional Machine Learning cannot deal with graph data in a satisfactory way, in fact they are designed to work with simpler data types like images, which can be represented as grids.
TANCON, GIULIA
core  

Simple and Efficient Heterogeneous Graph Neural Network

, 2023
Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
Fan, D, Yan, Mingyu, Yang, Xiaocheng, Pan, Shirui, Fan, Dongrui, Yan, M, Ye, X, Ye, Xiaochun, Yang, X, Pan, S   +9 more
core   +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