Multi-graph aggregated graph neural network for heterogeneous graph representation learning

Author:

Zhu ShuaileiORCID,Wang XiaofengORCID,Lai ShuaimingORCID,Chen Yuntao,Zhai WenchaoORCID,Quan Daying,Qi YuanyuanORCID,Lv LaishuiORCID

Funder

“Leading Goose” R\&D Program of Zhejiang Province

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. Yan Y, Li C, Yu Y et al (2023) OSGNN: Original graph and subgraph aggregated graph neural network. Expert Syst Appl 225:120115

2. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations, pp 548–549

3. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30:1024–1034

4. Sun C, Duan X, Qiu L et al (2022) RLIM: Representation learning method for influence maximization in social networks. Int J Mach Learn Cybern 13(11):3425–3440

5. Andreoletti D, Troia S, Musumeci F, et al (2019) Network traffic prediction based on diffusion convolutional recurrent neural networks. In: IEEE Conference on Computer Communications Workshops, pp 246–251

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