OSGNN: Original graph and Subgraph aggregated Graph Neural Network

Author:

Yan YeyuORCID,Li ChaoORCID,Yu YanweiORCID,Li XiangjuORCID,Zhao ZhongyingORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference39 articles.

1. Andreoletti, D., Troia, S., Musumeci, F., Giordano, S., Maier, G., & Tornatore, M. (2016). Network Traffic Prediction based on Diffusion Convolutional Recurrent Neural Networks. In Proceedings of the IEEE INFOCOM 2019-IEEE conference on computer communications workshops (pp. 246–251).

2. Atwood, J., & Towsley, D. (2016). Diffusion-Convolutional Neural Networks. In Proceedings of the neural information processing systems (pp. 1993–2001).

3. Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2014). Spectral Networks and Deep Locally Connected Networks on Graphs. In Proceedings of the 2nd international conference on learning representations.

4. HMSG: Heterogeneous graph neural network based on metapath subgraph learning;Cai,2021

5. MEGNN: Meta-path extracted graph neural network for heterogeneous graph representation learning;Chang;Knowledge-Based Systems,2022

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1. A Fast and Robust Attention-Free Heterogeneous Graph Convolutional Network;IEEE Transactions on Big Data;2024-10

2. Multi-graph aggregated graph neural network for heterogeneous graph representation learning;International Journal of Machine Learning and Cybernetics;2024-08-05

3. PNAHN: Parallel Neighbor Aggregation for Heterogeneous Graph Neural Network;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. MHGNN: Multi-view fusion based Heterogeneous Graph Neural Network;Applied Intelligence;2024-06-20

5. Subgraph representation learning with self-attention and free adversarial training;Applied Intelligence;2024-05-31

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