Random Walks on Huge Graphs at Cache Efficiency-Reference-Cited by-同舟云学术

Random Walks on Huge Graphs at Cache Efficiency

Published:2021-10-26 Issue: Volume: Page:
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Container-title:Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles CD-ROM
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Author:

Yang Ke¹,Ma Xiaosong²,Thirumuruganathan Saravanan²,Chen Kang³,Wu Yongwei³

Affiliation:

1. Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, China, Qatar Computing Research Institute, Hamad Bin Khalifa University and Beijing HaiZhi XingTu Technology Co., Ltd.

2. Qatar Computing Research Institute, Hamad Bin Khalifa University

3. Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, China and Beijing HaiZhi XingTu Technology Co., Ltd.

Funder

Natural Science Foundation of China

National Key Research & Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477132.3483575

Reference100 articles.

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2. Sami Abu-El-Haija Bryan Perozzi Rami Al-Rfou and Alexander A Alemi. 2018. Watch your step: Learning node embeddings via graph attention. In Advances in Neural Information Processing Systems. 9180--9190. Sami Abu-El-Haija Bryan Perozzi Rami Al-Rfou and Alexander A Alemi. 2018. Watch your step: Learning node embeddings via graph attention. In Advances in Neural Information Processing Systems. 9180--9190.

3. Alibaba. 2020. Euler. https://github.com/alibaba/euler Alibaba. 2020. Euler. https://github.com/alibaba/euler

4. Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: predicting and recommending links in social networks. In WSDM. 635--644. Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: predicting and recommending links in social networks. In WSDM. 635--644.

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