ABSTRACT
Data-intensive applications dominated by random accesses to large working sets fail to utilize the computing power of modern processors. Graph random walk, an indispensable workhorse for many important graph processing and learning applications, is one prominent case of such applications. Existing graph random walk systems are currently unable to match the GPU-side node embedding training speed.
This work reveals that existing approaches fail to effectively utilize the modern CPU memory hierarchy, due to the widely held assumption that the inherent randomness in random walks and the skewed nature of graphs render most memory accesses random. We demonstrate that there is actually plenty of spatial and temporal locality to harvest, by careful partitioning, rearranging, and batching of operations. The resulting system, FlashMob, improves both cache and memory bandwidth utilization by making memory accesses more sequential and regular. We also found that a classical combinatorial optimization problem (and its exact pseudo-polynomial solution) can be applied to complex decision making, for accurate yet efficient data/task partitioning. Our comprehensive experiments over diverse graphs show that our system achieves an order of magnitude performance improvement over the fastest existing system. It processes a 58GB real graph at higher per-step speed than the existing system on a 600KB toy graph fitting in the L2 cache.
- [n.d.]. Laboratory for Web Algorithmcs. http://law.di.unimi.it/datasets.php.Google Scholar
- 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.Google Scholar
- Alibaba. 2020. Euler. https://github.com/alibaba/eulerGoogle Scholar
- Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: predicting and recommending links in social networks. In WSDM. 635--644.Google Scholar
- Ziv Bar-Yossef, Alexander Berg, Steve Chien, Jittat Fakcharoenphol, and Dror Weitz. 2000. Approximating aggregate queries about web pages via random walks. In VLDB. 535--544.Google Scholar
- Scott Beamer, Krste Asanović, and David Patterson. 2017. Reducing pagerank communication via propagation blocking. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 820--831.Google ScholarCross Ref
- Paolo Boldi, Massimo Santini, and Sebastiano Vigna. 2008. A large time-aware web graph. SIGIR Forum 42, 2 (2008), 33--38.Google ScholarDigital Library
- Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering 30, 9 (2018), 1616--1637.Google ScholarDigital Library
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM international on conference on information and knowledge management. 891--900.Google ScholarDigital Library
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations.. In AAAI, Vol. 16. 1145--1152.Google Scholar
- Riccardo Cappuzzo, Paolo Papotti, and Saravanan Thirumuruganathan. 2020. Creating embeddings of heterogeneous relational datasets for data integration tasks. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 1335--1349.Google ScholarDigital Library
- Sandro Cavallari, Vincent W Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cambria. 2017. Learning community embedding with community detection and node embedding on graphs. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 377--386.Google ScholarDigital Library
- Rong Chen, Jiaxin Shi, Yanzhe Chen, and Haibo Chen. 2015. Power-Lyra: Differentiated graph computation and partitioning on skewed graphs. In Proceedings of the Tenth European Conference on Computer Systems. 1--15.Google ScholarDigital Library
- Xing Chen, MingXi Liu, and GuiYing Yan. 2012. Drug-target interaction prediction by random walk on the heterogeneous network. Molecular BioSystems 8, 7 (2012), 1970--1978.Google ScholarCross Ref
- Yan-Hao Chen, Ari B. Hayes, Chi Zhang, Timothy Salmon, and Eddy Z. Zhang. 2018. Locality-aware software throttling for sparse matrix operation on GPUs. In 2018 USENIX Annual Technical Conference (USENIX ATC 18). 413--426.Google Scholar
- Robert M Christley, GL Pinchbeck, Roger G Bowers, Damian Clancy, Nigel P French, Rachel Bennett, and Joanne Turner. 2005. Infection in social networks: using network analysis to identify high-risk individuals. American journal of epidemiology 162, 10 (2005), 1024--1031.Google Scholar
- Colin Cooper, Sang Hyuk Lee, Tomasz Radzik, and Yiannis Siantos. 2014. Random walks in recommender systems: exact computation and simulations. In Proceedings of the 23rd International Conference on World Wide Web. 811--816.Google ScholarDigital Library
- Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018. A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering 31, 5 (2018), 833--852.Google ScholarCross Ref
- Quanyu Dai, Qiang Li, Jian Tang, and Dan Wang. 2018. Adversarial network embedding. In 32nd AAAI Conference on Artificial Intelligence. 2167--2174.Google ScholarCross Ref
- Arjun Dasgupta, Gautam Das, and Heikki Mannila. 2007. A random walk approach to sampling hidden databases. In Proceedings of the 2007 ACM SIGMOD international conference on Management of data. 629--640.Google ScholarDigital Library
- Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Commun. ACM 51, 1 (2008), 107--113.Google ScholarDigital Library
- Luc Devroye. 2006. Nonuniform random variate generation. Handbooks in operations research and management science 13 (2006), 83--121.Google Scholar
- Laxman Dhulipala, Charles McGuffey, Hongbo Kang, Yan Gu, Guy E. Blelloch, Phillip B. Gibbons, and Julian Shun. 2020. Sage: parallel semi-asymmetric graph algorithms for NVRAMs. 13, 9 (2020), 1598--1613.Google Scholar
- Krzysztof Dudziński and Stanisław Walukiewicz. 1987. Exact methods for the knapsack problem and its generalizations. European Journal of Operational Research 28, 1 (1987), 3--21.Google ScholarCross Ref
- Peter Ebbes, Zan Huang, Arvind Rangaswamy, et al. 2010. Subgraph sampling methods for social networks: The good, the bad, and the ugly. Technical Report.Google Scholar
- Alessandro Epasto and Bryan Perozzi. 2019. Is a single embedding enough? Learning node representations that capture multiple social contexts. In The World Wide Web Conference. 394--404.Google ScholarDigital Library
- Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In The World Wide Web Conference. 417--426.Google ScholarDigital Library
- Prasun Gera, Hyojong Kim, Piyush Sao, Hyesoon Kim, and David Bader. 2020. Traversing large graphs on GPUs with unified memory. Proceedings of the VLDB Endowment 13, 7 (2020), 1119--1133.Google ScholarDigital Library
- Gurbinder Gill, Roshan Dathathri, Loc Hoang, Ramesh Peri, and Keshav Pingali. 2020. Single machine graph analytics on massive datasets using Intel optane DC persistent memory. In Proceedings of the VLDB Endowment, Vol. 13. 1304--1318.Google ScholarDigital Library
- Gurbinder Gill, Roshan Dathathri, Saeed Maleki, Madan Musuvathi, Todd Mytkowicz, and Olli Saarikivi. 2021. Distributed training of embeddings using graph analytics. In 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 973--983.Google ScholarCross Ref
- Minas Gjoka, Maciej Kurant, Carter T Butts, and Athina Markopoulou. 2010. Walking in facebook: A case study of unbiased sampling of osns. In IEEE INFOCOM. IEEE, 1--9.Google Scholar
- Joseph E. Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin. 2012. PowerGraph: Distributed graph-parallel computation on natural graphs. In the Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12). Hollywood, CA, 17--30.Google ScholarDigital Library
- Palash Goyal and Emilio Ferrara. 2018. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems 151 (2018), 78--94.Google ScholarCross Ref
- Aditya Grover and Jure Leskovec. 2016. Node2vec on Spark. https://github.com/aditya-grover/node2vec.Google Scholar
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855--864.Google ScholarDigital Library
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035.Google Scholar
- William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. IEEE Data(base) Engineering Bulletin 40 (2017), 52--74.Google Scholar
- Wook-Shin Han, Sangyeon Lee, Kyungyeol Park, Jeong-Hoon Lee, Min-Soo Kim, Jinha Kim, and Hwanjo Yu. 2013. TurboGraph: A fast parallel graph engine handling billion-scale graphs in a single PC. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 77--85.Google ScholarDigital Library
- Intel. 2009. VTune Performance Analyzer. https://software.intel.com/content/www/us/en/develop/home. html.Google Scholar
- Intel. 2020. Second Generation Intel Xeon Scalable Processors. https://www. intel.com/content/dam/www/public/us/en/documents/product-briefs/2nd-gen-xeon-scalable-processors-brief-Feb-2020-2.pdf.Google Scholar
- Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, and Ion Stoica. 2018. ASAP: Fast, approximate graph pattern mining at scale. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 745--761.Google Scholar
- Abhinav Jangda, Sandeep Polisetty, Arjun Guha, and Marco Serafini. 2021. Accelerating graph sampling for graph machine learning using GPUs. In Proceedings of the 16th European Conference on Computer Systems. ACM, 311--326.Google ScholarDigital Library
- Glen Jeh and Jennifer Widom. 2002. SimRank: A measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 538--543.Google ScholarDigital Library
- Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. 2017. Random walk based fake account detection in online social networks. In 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 273--284.Google ScholarCross Ref
- Zhihao Jia, Yongkee Kwon, Galen Shipman, Pat McCormick, Mattan Erez, and Alex Aiken. 2017. A distributed multi-GPU system for fast graph processing. Proceedings of the VLDB Endowment 11, 3 (2017), 297--310.Google ScholarDigital Library
- Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020. Improving the accuracy, scalability, and performance of graph neural networks with ROC. Proceedings of Machine Learning and Systems 2 (2020), 187--198.Google Scholar
- Nadav Kashtan, Shalev Itzkovitz, Ron Milo, and Uri Alon. 2004. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20, 11 (2004), 1746--1758.Google ScholarDigital Library
- Hans Kellerer, Ulrich Pferschy, and David Pisinger. 2004. The multiple-choice knapsack problem. In Knapsack Problems. Springer, 317--347.Google Scholar
- Maciej Kurant, Athina Markopoulou, and Patrick Thiran. 2010. On the bias of BFS (breadth first search). In 2010 22nd International Teletraffic Congress (lTC 22). IEEE, 1--8.Google ScholarCross Ref
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World Wide Web. ACM, 591--600.Google ScholarDigital Library
- Aapo Kyrola. 2013. Drunkardmob: Billions of random walks on just a PC. In Proceedings of the 7th ACM conference on Recommender systems. 257--264.Google ScholarDigital Library
- Aapo Kyrola, Guy Blelloch, and Carlos Guestrin. 2012. Graphchi: Large-scale graph computation on just a PC. In 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12). 31--46.Google Scholar
- Sangkeun Lee, Sang-il Song, Minsuk Kahng, Dongjoo Lee, and Sang-goo Lee. 2011. Random walk based entity ranking on graph for multidimensional recommendation. In Proceedings of the fifth ACM conference on Recommender systems. 93--100.Google ScholarDigital Library
- Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.Google Scholar
- LinkedIn. 2017. Random Walks on Large Scale Graphs with Apache Spark. https://www.slideshare.net/databricks/random-walks-on-large-scale-graphs-with-apache-spark-with-min-shen, Last accessed on 2020-12-10.Google Scholar
- Linux. 2009. perf. https://perf.wiki.kernel.org/.Google Scholar
- László Lovász et al. 1993. Random walks on graphs: A survey. Combinatorics, Paul erdos is eighty 2, 1 (1993), 1--46.Google Scholar
- Kathy Macropol, Tolga Can, and Ambuj K Singh. 2009. RRW: Repeated random walks on genome-scale protein networks for local cluster discovery. BMC bioinformatics 10, 1 (2009), 283.Google Scholar
- Grzegorz Malewicz, Matthew H Austern, Aart JC Bik, James C Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: A system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. 135--146.Google ScholarDigital Library
- Jasmina Malicevic, Subramanya Dulloor, Narayanan Sundaram, Nadathur Satish, Jeff Jackson, and Willy Zwaenepoel. 2015. Exploiting NVM in large-scale graph analytics. In Proceedings of the 3rd Workshop on Interactions of NVM/FLASH with Operating Systems and Workloads. 1--9.Google ScholarDigital Library
- George Marsaglia et al. 2003. Xorshift rngs. Journal of Statistical Software 8, 14 (2003), 1--6.Google Scholar
- Laurent Massoulié, Erwan Le Merrer, Anne-Marie Kermarrec, and Ayalvadi Ganesh. 2006. Peer counting and sampling in overlay networks: random walk methods. In Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing. 123--132.Google ScholarDigital Library
- Makoto Matsumoto and Takuji Nishimura. 1998. Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS) 8, 1 (1998), 3--30.Google ScholarDigital Library
- Alan Mislove, Massimiliano Marcon, Krishna P Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. 29--42.Google ScholarDigital Library
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab.Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701--710.Google ScholarDigital Library
- Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. 2019. NetSMF: Large-scale network embedding as sparse matrix factorization. In The World Wide Web Conference. 1509--1520.Google ScholarDigital Library
- Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 459--467.Google ScholarDigital Library
- Amitabha Roy, Ivo Mihailovic, and Willy Zwaenepoel. 2013. X-stream: Edge-centric graph processing using streaming partitions. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. 472--488.Google ScholarDigital Library
- Yousef Saad. 2003. Iterative methods for sparse linear systems. SIAM.Google Scholar
- Harold Herbert Seward. 1954. Information sorting in the application of electronic digital computers to business operations. Ph.D. Dissertation. Massachusetts Institute of Technology. Department of Electrical Engineering.Google Scholar
- Mo Sha, Yuchen Li, Bingsheng He, and Kian-Lee Tan. 2017. Accelerating dynamic graph analytics on GPUs. 11, 1 (2017), 107--120.Google Scholar
- Mo Sha, Yuchen Li, and Kian-Lee Tan. 2019. GPU-based graph traversal on compressed graphs. In Proceedings of the 2019 International Conference on Management of Data. 775--792.Google ScholarDigital Library
- Yingxia Shao, Shiyue Huang, Xupeng Miao, Bin Cui, and Lei Chen. 2020. Memory-aware framework for efficient second-order random walk on large graphs. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 1797--1812.Google ScholarDigital Library
- Aneesh Sharma, Jerry Jiang, Praveen Bommannavar, Brian Larson, and Jimmy Lin. 2016. GraphJet: Real-time content recommendations at Twitter. Proceedings of the VLDB Endowment 9, 13 (2016), 1281--1292.Google ScholarDigital Library
- Suraj Shetiya, Saravanan Thirumuruganathan, Nick Koudas, and Gautam Das. 2020. Astrid: Accurate selectivity estimation for string predicates using deep learning. Proceedings of the VLDB Endowment 14, 4 (2020), 471--484.Google ScholarDigital Library
- Julian Shun and Guy E Blelloch. 2013. Ligra: A lightweight graph processing framework for shared memory. In ACM Sigplan Notices, Vol. 48. ACM, 135--146.Google ScholarDigital Library
- Prabhakant Sinha and Andris A Zoltners. 1979. The multiple-choice knapsack problem. Operations Research 27, 3 (1979), 503--515.Google ScholarDigital Library
- Don Soltis, Irma Esmer, Adi Yoaz, and Sailesh Kottapalli. 2017. The New Intel Xeon Processor Scalable Family (Formerly Skylake-SP). In IEEE Hot Chips 32 Symposium.Google Scholar
- Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, and Yuchen Li. 2021. ThunderRW: An in-memory graph random walk engine.. In Proc. VLDB Endow., Vol. 14. 1992--2005.Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. 1067--1077.Google ScholarDigital Library
- Tencent. 2019. Plato. https://github.com/Tencent/platoGoogle Scholar
- Saravanan Thirumuruganathan, Nan Tang, Mourad Ouzzani, and AnHai Doan. 2020. Data curation with deep learning.. In EDBT. 277--286.Google Scholar
- Ke Tu, Peng Cui, Xiao Wang, Philip S Yu, and Wenwu Zhu. 2018. Deep recursive network embedding with regular equivalence. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2357--2366.Google ScholarDigital Library
- Alastair J Walker. 1977. An efficient method for generating discrete random variables with general distributions. ACM Transactions on Mathematical Software (TOMS) 3, 3 (1977), 253--256.Google ScholarDigital Library
- Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 1225--1234.Google ScholarDigital Library
- Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. GraphGAN: Graph representation learning with generative adversarial nets. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. 2508--2515.Google ScholarCross Ref
- Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018. Billion-scale commodity embedding for e-commerce recommendation in Alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 839--848.Google ScholarDigital Library
- Rui Wang, Yongkun Li, Hong Xie, Yinlong Xu, and John C. S. Lui. 2020. GraphWalker: An I/O-efficient and resource-friendly graph analytic system for fast and scalable random walks. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). 559--571.Google ScholarDigital Library
- Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017. Community preserving network embedding.. In AAAI, Vol. 17. 203--209.Google Scholar
- Wanjing Wei, Yangzihao Wang, Pin Gao, Shijie Sun, and Donghai Yu. 2020. A distributed multi-GPU system for large-scale node embedding at Tencent. arXiv preprint arXiv:2005.13789 (2020).Google Scholar
- Yahoo! 2002. Yahoo! AltaVista Web Page Hyperlink Connectivity Graph. https://webscope.sandbox.yahoo.com/catalog.php?datatype=gGoogle Scholar
- Jaewon Yang and Jure Leskovec. 2015. Defining and evaluating network communities based on ground-truth. Knowledge and Information Systems 42, 1 (2015), 181--213.Google ScholarDigital Library
- Ke Yang, MingXing Zhang, Kang Chen, Xiaosong Ma, Yang Bai, and Yong Jiang. 2019. KnightKing: A fast distributed graph random walk engine. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. 524--537.Google ScholarDigital Library
- Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Juncheng Liu, and Sourav S. Bhowmick. 2020. Scaling attributed network embedding to massive graphs. In Proceedings of the VLDB Endowment, Vol. 14. 37--49.Google ScholarDigital Library
- Dalong Zhang, Xin Huang, Ziqi Liu, Jun Zhou, Zhiyang Hu, Xianzheng Song, Zhibang Ge, Lin Wang, Zhiqiang Zhang, and Yuan Qi. 2020. AGL: A scalable system for industrial-purpose graph machine learning. 13, 12 (2020), 3125--3137.Google ScholarDigital Library
- Yunming Zhang, Vladimir Kiriansky, Charith Mendis, Saman Amarasinghe, and Matei Zaharia. 2017. Making caches work for graph analytics. In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 293--302.Google ScholarCross Ref
- Dongyan Zhou, Songjie Niu, and Shimin Chen. 2018. Efficient graph computation for node2vec. arXiv preprint arXiv:1805.00280 (2018).Google Scholar
- Xiaowei Zhu, Wenguang Chen, Weimin Zheng, and Xiaosong Ma. 2016. Gemini: A computation-centric distributed graph processing system. In the Proceedings of 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 301--316.Google Scholar
- Xiaojin Zhu, Andrew B Goldberg, Jurgen Van Gael, and David Andrzejewski. 2007. Improving diversity in ranking using absorbing random walks. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference. 97--104.Google Scholar
- Xiaowei Zhu, Wentao Han, and Wenguang Chen. 2015. GridGraph: Large-scale graph processing on a single machine using 2-level hierarchical partitioning. In USENIX ATC '15 Proceedings of the 2015 USENIX Conference on Usenix Annual Technical Conference. 375--386.Google Scholar
- Zhaocheng Zhu, Shizhen Xu, Jian Tang, and Meng Qu. 2019. GraphVite: A high-performance CPU-GPU hybrid system for node embedding. In The World Wide Web Conference. 2494--2504.Google ScholarDigital Library
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- Random Walks on Huge Graphs at Cache Efficiency
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