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While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed.
A linear time implementation of the reverse cuthill-mckee algorithm
Chan, W.-M. and George, A · 1980
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Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices
Karypis, G. and Kumar, V · 1997
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Learning spectral graph transformations for link prediction
Kunegis, J. and Lommatzsch, A · 2009
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Graph classification and clustering based on vector space embedding , volume 77
Riesen, K. and Bunke, H · 2010
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Think locally, act globally: Highly balanced graph partitioning
Sanders, P. and Schulz, C · 2013
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Defining and evaluating network communities based on ground-truth
Yang, J. and Leskovec, J · 2015
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Rabbit order: Just-in-time parallel reordering for fast graph analysis
Arai, J., Shiokawa, H., Yamamuro, T., Onizuka, M., and Iwamura, S · 2016
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Learning graph representations with embedding propagation
Garcia Duran, A. and Niepert, M · 2017
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Knowledge transfer for out-of-knowledge-base entities: a graph neural network approach
Hamaguchi, T., Oiwa, H., Shimbo, M., and Matsumoto, Y · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Fastgcn: Fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2018
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Heterogeneous graph neural networks for malicious account detection
Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., and Song, L · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. v. d., Titov, I., and Welling, M · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Deep reasoning with knowledge graph for social relationship understanding
Wang, Z., Chen, T., Ren, J. S., Yu, W., Cheng, H., and Lin, L · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
Cited alongside, same era.
Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Gnnadvisor: An adaptive and efficient runtime system for gnn acceleration on gpus
Wang, Y., Feng, B., Li, G., Li, S., Deng, L., Xie, Y., and Ding, Y · 2020
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Hardware acceleration of large scale gcn inference
Zhang, B., Zeng, H., and Prasanna, V · 2020
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Distdgl: distributed graph neural network training for billion-scale graphs
Zheng, D., Ma, C., Wang, M., Zhou, J., Su, Q., Song, X., Gan, Q., Zhang, Z., and Karypis, G · 2020
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https://github.com/dmlc/dgl/blob/master/examples/pytorch/rgcn-hetero/entity_classify_mb.py , 2021
Dgl code snippets · 2021
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https://github.com/quiver-team/torch-quiver , 2021
Quiver · 2021
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Fey, M. and Lenssen, J. E · 2019
Cited alongside, same era.
{ \{ NeuGraph } \} : Parallel deep neural network computation on large graphs
Ma, L., Yang, Z., Miao, Y., Xue, J., Wu, M., Zhou, L., and Dai, Y · 2019
Cited alongside, same era.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., and Zhang, Z · 2019
Cited alongside, same era.
Aligraph: A comprehensive graph neural network platform
Yang, H · 2019
Cited alongside, same era.
fusegnn: accelerating graph convolutional neural network training on gpgpu
Chen, Z., Yan, M., Zhu, M., Deng, L., Li, G., Li, S., and Xie, Y · 2020
Cited alongside, same era.
Graph neural networks for covid-19 drug discovery
Cheung, M. and Moura, J. M · 2020
Cited alongside, same era.
Estimating GPU Memory Consumption of Deep Learning Models , pp. 1342–1352
Gao, Y., Liu, Y., Zhang, H., Li, Z., Zhu, Y., Lin, H., and Yang, M · 2020
Cited alongside, same era.
Fey, M., Lenssen, J. E., Weichert, F., and Leskovec, J · 2021
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Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B., Vickers, R., Roberts, C., Tang, J., et al · 2021
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Bgl: Gpu-efficient gnn training by optimizing graph data i/o and preprocessing
Liu, T., Chen, Y., Li, D., Wu, C., Zhu, Y., He, J., Peng, Y., Chen, H., Chen, H., and Guo, C · 2021
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Graph neural networks for heterogeneous trust based social recommendation
Mandal, S. and Maiti, A · 2021
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Min, S. W., Wu, K., Huang, S., Hidayetoğlu, M., Xiong, J., Ebrahimi, E., Chen, D., and Hwu, W.-m · 2021
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Dorylus: Affordable, scalable, and accurate { \{ GNN } \} training with distributed { \{ CPU } \} servers and serverless threads
Thorpe, J., Qiao, Y., Eyolfson, J., Teng, S., Hu, G., Jia, Z., Wei, J., Vora, K., Netravali, R., Kim, M., et al · 2021
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Pcgraph: Accelerating gnn inference on large graphs via partition caching
Zhang, L., Lai, Z., Tang, Y., Li, D., Liu, F., and Luo, X · 2021
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Accelerating large scale real-time gnn inference using channel pruning
Zhou, H., Srivastava, A., Zeng, H., Kannan, R., and Prasanna, V · 2021
Later among the works it cites.
https://github.com/warai-0toko/Exact/blob/main/mem_speed_bench/models/sage.py , 2022
Exact code snippets · 2022
Closest in time.
Designing the topology of graph neural networks: A novel feature fusion perspective
Wei, L., Zhao, H., and He, Z · 2022
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