GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware

Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’networks. nature 393

1998

Earlier work this paper cites.

Denning, P.J.: The locality principle. In: Communication Networks And Computer Systems: A Tribute to Professor Erol Gelenbe (2006)

2006

Earlier work this paper cites.

Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20

2008

Earlier work this paper cites.

Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., & Eliassi-Rad, T.: Collective classification in network data. AI magazine 29

2008

Earlier work this paper cites.

Easley, D., & Kleinberg, J.: Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge university press (2010)

2010

Earlier work this paper cites.

Leskovec, J., & Sosič, R.: Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST) 8

2016

Earlier work this paper cites.

Kipf, T. N., & Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)

2017

Earlier work this paper cites.

Hamilton, W., Ying Z., & Leskovec, J.: Inductive representation learning on large graphs. Advances in neural information processing systems 30

2017

Earlier work this paper cites.

Zitnik, M., & Leskovec, J.: Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33

2017

Earlier work this paper cites.

Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. V. D., Titov, I., & Welling, M.: Modeling relational data with graph convolutional networks. In: European semantic web conference (2018)

2018

Cited alongside, same era.

Chen, J., Zhu, J., & Song, L.: Stochastic training of graph convolutional networks with variance reduction. In: ICML (2018)

2018

Cited alongside, same era.

Chen, J., Ma, T., & Xiao, C.: Fastgcn: fast learning with graph convolutional networks via importance sampling. In: ICLR (2018)

2018

Cited alongside, same era.

Huang, W., Zhang, T., Rong, Y., & Huang, J.: Adaptive sampling towards fast graph representation learning. Advances in neural information processing system 31

2018

Cited alongside, same era.

Mukkara, A., Beckmann, N., Abeydeera, M., et al.: Exploiting locality in graph analytics through hardware-accelerated traversal scheduling. In: MICRO (2018)

Zeng, H., Zhou, H., Srivastava, A., Kannan, R., & Prasanna, V.: Accurate, efficient and scalable graph embedding. In: IPDPS (2019)

2019

Later among the works it cites.

Ma, L., Yang, Z., Miao, Y., et al.: NeuGraph: Parallel Deep Neural Network Computation on Large Graphs. In: USENIX ATC 19 (2019)

2019

Later among the works it cites.

Zeng, H., Zhou, H., Srivastava, A., Kannan, R., & Prasanna, V.: Graphsaint: Graph sampling based inductive learning method. In: ICLR (2020)

2020

Later among the works it cites.

Yan, M., Deng, L., Hu, X., Liang, L., Feng, Y., Ye, X., Zhang, Z., Fan, D., Xie, Y.: Hygcn: A gcn accelerator with hybrid architecture. In: HPCA (2020)

2020

Later among the works it cites.

Yan, M., Chen, Z., Deng, L., et al.: Characterizing and understanding gcns on gpu. IEEE Computer Architecture Letters 19

2020

Later among the works it cites.