Zeting Pan
- Zhang X C, Wu C K, Yang Z J, MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction[J]. Briefings in Bioinformatics, 2021.Google Scholar
Cross Ref
- Skardinga J, Gabrys B, Musial K. Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey[J]. IEEE Access, 2021.Google ScholarCross Ref
- Sahu S, Mhedhbi A, Salihoglu S, The ubiquity of large graphs and surprising challenges of graph processing[J]. Proceedings of the VLDB Endowment, 2017, 11(4): 420-431.Google ScholarDigital Library
- Sahu S, Mhedhbi A, Salihoglu S, The ubiquity of large graphs and surprising challenges of graph processing: extended survey[J]. The VLDB Journal, 2020, 29(2): 595-618.Google ScholarCross Ref
- Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.Google Scholar
- Veličković P, Cucurull G, Casanova A, Graph attention networks[J]. arXiv preprint arXiv:1710.10903, 2017.Google Scholar
- Dwivedi V P, Joshi C K, Laurent T, Benchmarking graph neural networks[J]. arXiv preprint arXiv:2003.00982, 2020.Google Scholar
- Zaharia M, Chowdhury M, Franklin M J, Spark: Cluster computing with working sets[J]. HotCloud, 2010, 10(10-10): 95.Google Scholar
- Jiang J, Xiao P, Yu L, PSGraph: How Tencent trains extremely large-scale graphs with Spark?[C]//2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020: 1549-1557.Google Scholar
- Gonzalez J E, Xin R S, Dave A, Graphx: Graph processing in a distributed dataflow framework[C]//11th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 14). 2014: 599-613.Google Scholar
- Rossi E, Chamberlain B, Frasca F, Temporal graph networks for deep learning on dynamic graphs[J]. arXiv preprint arXiv:2006.10637, 2020.Google Scholar
- Malewicz G, Austern M H, Bik A J C, Pregel: a system for large-scale graph processing[C]//Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. 2010: 135-146.Google Scholar
- Gonzalez J E, Low Y, Gu H, Powergraph: Distributed graph-parallel computation on natural graphs[C]//10th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 12). 2012: 17-30.Google Scholar
- Chen Q, Bai S, Li Z, GraphHP: A hybrid platform for iterative graph processing[J]. arXiv preprint arXiv:1706.07221, 2017.Google Scholar
- Wang Z, Gu Y, Bao Y, Hybrid pulling/pushing for i/o-efficient distributed and iterative graph computing[C]//Proceedings of the 2016 International Conference on Management of Data. 2016: 479-494.Google Scholar
- Abbas Z, Kalavri V, Carbone P, Streaming graph partitioning: an experimental study[J]. Proceedings of the VLDB Endowment, 2018, 11(11): 1590-1603.Google ScholarDigital Library
- Tang J, Xu M, Fu S, A scheduling optimization technique based on reuse in spark to defend against apt attack[J]. Tsinghua Science and Technology, 2018, 23(5): 550-560.Google ScholarCross Ref
- Kumar S, Zhang X, Leskovec J. Predicting dynamic embedding trajectory in temporal interaction networks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1269-1278.Google Scholar
- Wang X, Lyu D, Li M, APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding[C]//Proceedings of the 2021 International Conference on Management of Data. 2021: 2628-2638.Google Scholar
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