Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs

Author:

Shao Yingxia1,Huang Shiyue2,Miao Xupeng2,Cui Bin2,Chen Lei3

Affiliation:

1. Beijing Univeristy of Posts and Telecommunications, Beijing, China

2. Peking University, Beijing, China

3. Hong Kong University of Science and Technology, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Hong Kong RGC GRF Project

Publisher

ACM

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. TEA: A General-Purpose Temporal Graph Random Walk Engine;Proceedings of the Eighteenth European Conference on Computer Systems;2023-05-08

2. LightTraffic: On Optimizing CPU-GPU Data Traffic for Efficient Large-scale Random Walks;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

3. NosWalker: A Decoupled Architecture for Out-of-Core Random Walk Processing;Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3;2023-03-25

4. Scalable Graph Sampling on GPUs with Compressed Graph;Proceedings of the 31st ACM International Conference on Information & Knowledge Management;2022-10-17

5. Discovering a cohesive football team through players’ attributed collaboration networks;Applied Intelligence;2022-10-12

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