GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software

P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, pp. 93–93, 2008

2008

Earlier work this paper cites.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “Tensorflow: A system for large-scale machine learning,” in OSDI , 2016, pp. 265–283

2016

Earlier work this paper cites.

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017

2017

Earlier work this paper cites.

L. Chen, Y. Liu, Z. Zheng, and P. Yu, “Heterogeneous neural attentive factorization machine for rating prediction,” in CIKM . ACM, 2018, pp. 833–842

2018

Earlier work this paper cites.

R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in KDD , 2018, pp. 974–983

2018

Earlier work this paper cites.

P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph Attention Networks,” ICLR , 2018

2018

Earlier work this paper cites.

J. Zhang, X. Wang, H. Zhang, H. Sun, K. Wang, and X. Liu, “A novel neural source code representation based on abstract syntax tree,” in ICSE . IEEE / ACM, 2019, pp. 783–794

2019

Cited alongside, same era.

N. D. Q. Bui, Y. Yu, and L. Jiang, “Autofocus: Interpreting attention-based neural networks by code perturbation,” in ASE . IEEE, 2019, pp. 38–41

2019

Cited alongside, same era.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” in NeurIPS , 2019, pp. 8026–8037

2019

Cited alongside, same era.

M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019

2019

Cited alongside, same era.

M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song, J. Zhou, C. Ma, L. Yu, Y. Gai, T. Xiao, T. He, G. Karypis, J. Li, and Z. Zhang, “Deep graph library: A graph-centric, highly-performant package for graph neural networks,” arXiv preprint arXiv:1909.01315 , 2019

F. Wu, A. H. S. Jr., T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger, “Simplifying graph convolutional networks,” in ICML , vol. 97. PMLR, 2019, pp. 6861–6871

2019

Later among the works it cites.

A. LeClair, S. Haque, L. Wu, and C. McMillan, “Improved code summarization via a graph neural network,” in ICPC . ACM, 2020, pp. 184–195

2020

Later among the works it cites.

D. Grattarola and C. Alippi, “Graph neural networks in tensorflow and keras with spektral,” arXiv preprint arXiv:2006.12138 , 2020

Original

2020

Later among the works it cites.

L. Chen, J. Li, J. Peng, T. Xie, Z. Cao, K. Xu, X. He, and Z. Zheng, “A survey of adversarial learning on graph,” arXiv preprint arXiv:2003.05730 , 2020

Original

2020

Later among the works it cites.

L. Chen, Y. Liu, X. He, L. Gao, and Z. Zheng, “Matching user with item set: Collaborative bundle recommendation with deep attention network,” in IJCAI , 2019, pp. 2095–2101