Results 11 to 20 of about 110,849 (310)

Graph Neural Networks for Graph Drawing

IEEE Transactions on Neural Networks and Learning Systems, 2022
Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has paved the road to the massive usage of Gradient ...
Matteo Tiezzi, Tiezzi M., Gori, Marco, Ciravegna, Gabriele, Gabriele Ciravegna, Marco Gori, Gori M., Ciravegna G., Tiezzi, Matteo   +8 more
core   +8 more sources

Motif Graph Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2023
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph ...
Wu, Min, Cai, Ruichu, Hao, Zhifeng, Chen, Xuexin, Fang, Yuan, Li, Zijian, CAI, Ruicui   +6 more
core   +5 more sources

Bounded graph clustering with graph neural networks

Journal of Physics: Complexity
In community detection, many methods require the user to specify the number of clusters in advance since an exhaustive search over all possible values is computationally infeasible.
Kibidi Neocosmos, Diego Baptista, Nicole Ludwig   +2 more
doaj   +3 more sources

Binarized graph neural network [PDF]

World Wide Web, 2021
Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based ...
Hanchen Wang 0001, Defu Lian, Ying Zhang 0001, Lu Qin 0001, Xiangjian He, Yiguang Lin, Xuemin Lin 0001   +6 more
openaire   +2 more sources

Curvature graph neural network [PDF]

Information Sciences, 2022
Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism.
Haifeng Li 0007, Jun Cao, Jiawei Zhu, Yu Liu 0003, Qing Zhu 0012, Guohua Wu 0001   +5 more
openaire   +2 more sources

Network In Graph Neural Network

CoRR, 2021
Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and knowledge graph reasoning. In this regard, various strategies have been proposed in the past to improve the expressiveness of GNNs.
Xiang Song 0003, Runjie Ma, Jiahang Li 0002, Muhan Zhang, David Paul Wipf   +4 more
openaire   +2 more sources

Stochastic Graph Neural Networks [PDF]

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
openaire   +2 more sources

Learning the Network of Graphs for Graph Neural Networks

CoRR, 2022
Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs: graphs are unknown, nodes have noisy features, and graphs contain noisy connections. Aiming at solving these problems, we propose a new graph neural network named as GL-GNN.
Yixiang Shan, Jielong Yang, Xing Liu 0009, Yixing Gao, Hechang Chen, Shuzhi Sam Ge   +5 more
openaire   +2 more sources

Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

2023 IEEE International Conference on Big Data (BigData), 2023
This paper is submitted and will be published on Big Data Conference 2023, Data-driven Science for Graphs: Algorithms, Architectures, and Application ...
Zheyi Qin, Randy C. Paffenroth, Anura P. Jayasumana   +2 more
openaire   +2 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, Andrey Berdnikov, Pavel Ustin   +2 more
doaj   +1 more source