research-article

Public Access

AutoMine: harmonizing high-level abstraction and high performance for graph mining

Authors:

Daniel Mawhirter,

Bo WuAuthors Info & Claims

SOSP '19: Proceedings of the 27th ACM Symposium on Operating Systems Principles

Pages 509 - 523

https://doi.org/10.1145/3341301.3359633

Published: 27 October 2019 Publication History

Abstract

Graph mining algorithms that aim at identifying structural patterns of graphs are typically more complex than graph computation algorithms such as breadth first search. Researchers have implemented several systems with high-level and flexible interfaces customized for tackling graph mining problems. However, we find that for triangle counting, one of the simplest graph mining problems, such systems can be several times slower than a single-threaded implementation of a straightforward algorithm.

In this paper, we reveal the root causes of the severe inefficiencies of state-of-the-art graph mining systems and the challenges to address the performance problems. We build AutoMine, a single-machine system to provide both high-level interfaces and high performance for large-scale graph mining applications. The novelty of AutoMine comes from 1) a new representation of subgraph patterns and 2) compilation techniques that automatically generate efficient mining code with minimized memory consumption from a high-level abstraction. We have extensively evaluated AutoMine against 3 graph mining systems on 8 real-world graphs of different scales. Our experimental results show that AutoMine often produces several orders of magnitude better performance and can process very large graphs existing systems cannot handle.

References

[1]

Apache giraph. http://giraph.apache.org/.

[2]

Orkut social network. http://snap.stanford.edu/data/com-Orkut.html.

[3]

Ehab Abdelhamid, Ibrahim Abdelaziz, Panos Kalnis, Zuhair Khayyat, and Fuad Jamour. Scalemine: scalable parallel frequent subgraph mining in a single large graph. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016, Salt Lake City, UT, USA, November 13--18, 2016, pages 716--727, 2016.

[4]

Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, and Nick G. Duffield. Efficient graphlet counting for large networks. In 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14--17, 2015, pages 1--10, 2015.

Digital Library

[5]

Zhiyuan Ai, Mingxing Zhang, Yongwei Wu, Xuehai Qian, Kang Chen, and Weimin Zheng. Squeezing out all the value of loaded data: An out-of-core graph processing system with reduced disk I/O. In 2017 USENIX Annual Technical Conference, USENIX ATC 2017, Santa Clara, CA, USA, July 12--14, 2017., pages 125--137, 2017.

[6]

Maryam Aliakbarpour, Amartya Shankha Biswas, Themitstoklis Gouleakis, John Peebles, Ronitt Rubinfeld, and Anak Yodpinyanee. Sublinear-time algorithms for counting star subgraphs with applications to join selectivity estimation. CoRR, abs/1601.04233, 2016.

[7]

Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '06, pages 44--54, New York, NY, USA, 2006. ACM.

Digital Library

[8]

Paolo Boldi, Marco Rosa, Massimo Santini, and Sebastiano Vigna. Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks. In Sadagopan Srinivasan, Krithi Ramamritham, Arun Kumar, M. P. Ravindra, Elisa Bertino, and Ravi Kumar, editors, Proceedings of the 20th international conference on World Wide Web, pages 587--596. ACM Press, 2011.

[9]

Paolo Boldi and Sebastiano Vigna. The webgraph framework I: compression techniques. In Proceedings of the 13th international conference on World Wide Web, WWW 2004, New York, NY, USA, May 17--20, 2004, pages 595--602, 2004.

Digital Library

[10]

Hongzhi Chen, Miao Liu, Yunjian Zhao, Xiao Yan, Da Yan, and James Cheng. G-miner: an efficient task-oriented graph mining system. In Proceedings of the Thirteenth EuroSys Conference, EuroSys 2018, Porto, Portugal, April 23--26, 2018, pages 32:1--32:12, 2018.

Digital Library

[11]

Rong Chen, Jiaxin Shi, Yanzhe Chen, and Haibo Chen. Powerlyra: differentiated graph computation and partitioning on skewed graphs. In Proceedings of the Tenth European Conference on Computer Systems, EuroSys 2015, Bordeaux, France, April 21--24, 2015, pages 1:1--1:15, 2015.

Digital Library

[12]

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Q. Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. TVM: an automated end-to-end optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018, Carlsbad, CA, USA, October 8--10, 2018., pages 578--594, 2018.

[13]

Talya Eden, Amit Levi, Dana Ron, and C. Seshadhri. Approximately counting triangles in sublinear time. In FOCS, 2015.

Digital Library

[14]

Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: frequent subgraph and pattern mining in a single large graph. PVLDB, 7(7):517--528, 2014.

Digital Library

[15]

Wenfei Fan, Wenyuan Yu, Jingbo Xu, Jingren Zhou, Xiaojian Luo, Qiang Yin, Ping Lu, Yang Cao, and Ruiqi Xu. Parallelizing sequential graph computations. ACM Trans. Database Syst., 43(4):18:1--18:39, December 2018.

Digital Library

[16]

Gurbinder Gill, Roshan Dathathri, Loc Hoang, Andrew Lenharth, and Keshav Pingali. Abelian: A compiler for graph analytics on distributed, heterogeneous platforms. In Euro-Par 2018: Parallel Processing - 24th International Conference on Parallel and Distributed Computing, Turin, Italy, August 27--31, 2018, Proceedings, pages 249--264, 2018.

[17]

Gurbinder Gill, Roshan Dathathri, Loc Hoang, and Keshav Pingali. A study of partitioning policies for graph analytics on large-scale distributed platforms. PVLDB, 12(4):321--334, 2018.

Digital Library

[18]

Joseph E. Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin. Powergraph: Distributed graph-parallel computation on natural graphs. In 10th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2012, Hollywood, CA, USA, October 8--10, 2012, pages 17--30, 2012.

[19]

Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, and Ion Stoica. Graphx: Graph processing in a distributed dataflow framework. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14), pages 599--613, Broomfield, CO, 2014. USENIX Association.

Digital Library

[20]

Wei Han, Daniel Mawhirter, Matthew Buland, and Bo Wu. Graphie: Large-scale asynchronous graph traversals on just a gpu. In International Conference on Parallel Architectures and Compilation Techniques, PACT 2017, Portland, Oregon, USA, September 9--13, 2017.

[21]

Wook-Shin Han, Sangyeon Lee, Kyungyeol Park, Jeong-Hoon Lee, Min-Soo Kim, Jinha Kim, and Hwanjo Yu. Turbograph: a fast parallel graph engine handling billion-scale graphs in a single PC. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11--14, 2013, pages 77--85, 2013.

Digital Library

[22]

Tomaz Hocevar and Janez Demsar. A combinatorial approach to graphlet counting. Bioinformatics, 30(4):559--565, 2014.

[23]

Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, and Ion Stoica. ASAP: Fast, approximate graph pattern mining at scale. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pages 745--761, Carlsbad, CA, 2018. USENIX Association.

Digital Library

[24]

Jeremy Kepner, Peter Aaltonen, David A. Bader, Aydin Buluç, Franz Franchetti, John R. Gilbert, Dylan Hutchison, Manoj Kumar, Andrew Lumsdaine, Henning Meyerhenke, Scott McMillan, Carl Yang, John D. Owens, Marcin Zalewski, Timothy G. Mattson, and José E. Moreira. Mathematical foundations of the graphblas. In 2016 IEEE High Performance Extreme Computing Conference, HPEC 2016, Waltham, MA, USA, September 13--15, 2016, pages 1--9, 2016.

[25]

Aapo Kyrola, Guy Blelloch, and Carlos Guestrin. Graphchi: Large-scale graph computation on just a pc. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, OSDI'12, pages 31--46, 2012.

Digital Library

[26]

Jure Leskovec, Jon M. Kleinberg, and Christos Faloutsos. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, August 21--24, 2005, pages 177--187, 2005.

Digital Library

[27]

Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, and Jonathan Ragan-Kelley. Differentiable programming for image processing and deep learning in halide. ACM Trans. Graph., 37(4):139:1--139:13, 2018.

Digital Library

[28]

Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, and Joseph M. Hellerstein. Distributed graphlab: A framework for machine learning in the cloud. PVLDB, 5(8):716--727, 2012.

Digital Library

[29]

Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. Pregel: A system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pages 135--146, 2010.

Digital Library

[30]

D. Marcus and Y. Shavitt. Rage - a rapid graphlet enumerator for large networks. Comput. Netw., 56(2):810--819, February 2012.

Digital Library

[31]

Daniel Mawhirter, Bo Wu, Dinesh Mehta, and Chao Ai. Approxg: Fast approximate parallel graphlet counting through accuracy control. In 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018, Washington, DC, USA, May 1--4, 2018, pages 533--542, 2018.

Digital Library

[32]

Frank McSherry, Michael Isard, and Derek Gordon Murray. Scalability! but at what cost? In 15th Workshop on Hot Topics in Operating Systems, HotOS XV, Kartause Ittingen, Switzerland, May 18--20, 2015, 2015.

[33]

Derek Gordon Murray, Frank McSherry, Rebecca Isaacs, Michael Isard, Paul Barham, and Martín Abadi. Naiad: a timely dataflow system. In ACM SIGOPS 24th Symposium on Operating Systems Principles, SOSP '13, Farmington, PA, USA, November 3--6, 2013, pages 439--455, 2013.

Digital Library

[34]

Donald Nguyen, Andrew Lenharth, and Keshav Pingali. A lightweight infrastructure for graph analytics. In ACM SIGOPS 24th Symposium on Operating Systems Principles, SOSP '13, Farmington, PA, USA, November 3--6, 2013, pages 456--471, 2013.

Digital Library

[35]

Sreepathi Pai and Keshav Pingali. A compiler for throughput optimization of graph algorithms on gpus. In Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2016, part of SPLASH 2016, Amsterdam, The Netherlands, October 30 - November 4, 2016, pages 1--19, 2016.

Digital Library

[36]

A. Pavan, Kanat Tangwongsan, Srikanta Tirthapura, and Kun-Lung Wu. Counting and sampling triangles from a graph stream. PVLDB, 6(14):1870--1881, 2013.

Digital Library

[37]

Zhen Peng, Alexander Powell, Bo Wu, Tekin Bicer, and Bin Ren. Graphphi: efficient parallel graph processing on emerging throughput-oriented architectures. In Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques, PACT 2018, Limassol, Cyprus, November 01--04, 2018, pages 9:1--9:14, 2018.

Digital Library

[38]

Mahmudur Rahman, Mansurul Bhuiyan, and Mohammad Al Hasan. Graft: An approximate graphlet counting algorithm for large graph analysis. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12, pages 1467--1471, 2012.

[39]

Amitabha Roy, Laurent Bindschaedler, Jasmina Malicevic, and Willy Zwaenepoel. Chaos: Scale-out graph processing from secondary storage. In Proceedings of the 25th Symposium on Operating Systems Principles, SOSP '15, pages 410--424, 2015.

Digital Library

[40]

Amitabha Roy, Ivo Mihailovic, and Willy Zwaenepoel. X-stream: Edge-centric graph processing using streaming partitions. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP '13, pages 472--488, 2013.

Digital Library

[41]

Jiaxin Shi, Youyang Yao, Rong Chen, Haibo Chen, and Feifei Li. Fast and concurrent RDF queries with rdma-based distributed graph exploration. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016., pages 317--332, 2016.

Digital Library

[42]

Julian Shun and Guy E. Blelloch. Ligra: A lightweight graph processing framework for shared memory. In Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP '13, pages 135--146, 2013.

Digital Library

[43]

Julian Shun and Kanat Tangwongsan. Multicore triangle computations without tuning. In 31st IEEE International Conference on Data Engineering, ICDE 2015, Seoul, South Korea, April 13--17, 2015, pages 149--160, 2015.

[44]

Narayanan Sundaram, Nadathur Satish, Md Mostofa Ali Patwary, Subramanya R. Dulloor, Michael J. Anderson, Satya Gautam Vadlamudi, Dipankar Das, and Pradeep Dubey. Graphmat: High performance graph analytics made productive. Proc. VLDB Endow., 8(11), July 2015.

[45]

Nilothpal Talukder and Mohammed J. Zaki. A distributed approach for graph mining in massive networks. Data Min. Knowl. Discov., 30(5):1024--1052, 2016.

Digital Library

[46]

Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini, Georgos Siganos, Mohammed J. Zaki, and Ashraf Aboulnaga. Arabesque: a system for distributed graph mining. In Proceedings of the 25th Symposium on Operating Systems Principles, SOSP 2015, Monterey, CA, USA, October 4--7, 2015, pages 425--440, 2015.

Digital Library

[47]

Chad Voegele, Yi-Shan Lu, Sreepathi Pai, and Keshav Pingali. Parallel triangle counting and k-truss identification using graph-centric methods. In 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017, Waltham, MA, USA, September 12--14, 2017, pages 1--7, 2017.

[48]

Chad Voegele, Yi-Shan Lu, Sreepathi Pai, and Keshav Pingali. Parallel triangle counting and k-truss identification using graph-centric methods. In 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017, Waltham, MA, USA, September 12--14, 2017, pages 1--7, 2017.

[49]

Keval Vora, Guoqing Xu, and Rajiv Gupta. Load the edges you need: A generic i/o optimization for disk-based graph processing. In 2016 USENIX Annual Technical Conference (USENIX ATC 16), pages 507--522, Denver, CO, 2016. USENIX Association.

Digital Library

[50]

Kai Wang, Guoqing (Harry) Xu, Zhendong Su, and Yu David Liu. Graphq: Graph query processing with abstraction refinement - scalable and programmable analytics over very large graphs on a single PC. In 2015 USENIX Annual Technical Conference, USENIX ATC '15, July 8--10, Santa Clara, CA, USA, pages 387--401, 2015.

[51]

Kai Wang, Zhiqiang Zuo, John Thorpe, Tien Quang Nguyen, and Guoqing Harry Xu. Rstream: Marrying relational algebra with streaming for efficient graph mining on A single machine. In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018, Carlsbad, CA, USA, October 8--10, 2018., pages 763--782, 2018.

[52]

Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D. Owens. Gunrock: A high-performance graph processing library on the gpu. In Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015, pages 265--266, New York, NY, USA, 2015. ACM.

Digital Library

[53]

Sebastian Wernicke and Florian Rasche. Fanmod: A tool for fast network motif detection. Bioinformatics, 22(9):1152--1153, May 2006.

Digital Library

[54]

Ming Wu, Fan Yang, Jilong Xue, Wencong Xiao, Youshan Miao, Lan Wei, Haoxiang Lin, Yafei Dai, and Lidong Zhou. Gram: scaling graph computation to the trillions. In Proceedings of the Sixth ACM Symposium on Cloud Computing, SoCC 2015, Kohala Coast, Hawaii, USA, August 27--29, 2015, pages 408--421, 2015.

Digital Library

[55]

Da Yan, Hongzhi Chen, James Cheng, M. Tamer Özsu, Qizhen Zhang, and John C. S. Lui. G-thinker: Big graph mining made easier and faster. CoRR, abs/1709.03110, 2017.

[56]

Da Yan, James Cheng, Yi Lu, and Wilfred Ng. Blogel: A block-centric framework for distributed computation on real-world graphs. PVLDB, 7(14):1981--1992, 2014.

Digital Library

[57]

Jaewon Yang and Jure Leskovec. Defining and evaluating network communities based on ground-truth. Knowledge and Information Systems, 42(1):181--213, 2015.

Digital Library

[58]

Feng Zhang, Bo Wu, Jidong Zhai, Bingsheng He, and Wenguang Chen. Finepar: Irregularity-aware fine-grained workload partitioning on integrated architectures. In The International Symposium on Code Generation and Optimization, 2017.

[59]

Kaiyuan Zhang, Rong Chen, and Haibo Chen. Numa-aware graph-structured analytics. In Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015, San Francisco, CA, USA, February 7--11, 2015, pages 183--193, 2015.

Digital Library

[60]

Mingxing Zhang, Yongwei Wu, Kang Chen, Xuehai Qian, Xue Li, and Weimin Zheng. Exploring the hidden dimension in graph processing. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016., pages 285--300, 2016.

[61]

Mingxing Zhang, Yongwei Wu, Youwei Zhuo, Xuehai Qian, Chengying Huan, and Kang Chen. Wonderland: A novel abstraction-based out-of-core graph processing system. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2018, Williamsburg, VA, USA, March 24--28, 2018, pages 608--621, 2018.

Digital Library

[62]

Yunming Zhang, Mengjiao Yang, Riyadh Baghdadi, Shoaib Kamil, Julian Shun, and Saman P. Amarasinghe. Graphit: a high-performance graph DSL. PACMPL, 2(OOPSLA):121:1--121:30, 2018.

[63]

Da Zheng, Disa Mhembere, Randal C. Burns, Joshua T. Vogelstein, Carey E. Priebe, and Alexander S. Szalay. Flashgraph: Processing billion-node graphs on an array of commodity ssds. In Proceedings of the 13th USENIX Conference on File and Storage Technologies, FAST 2015, Santa Clara, CA, USA, February 16--19, 2015, pages 45--58, 2015.

[64]

Xiaowei Zhu, Wenguang Chen, Weimin Zheng, and Xiaosong Ma. Gemini: A computation-centric distributed graph processing system. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pages 301--316, Savannah, GA, 2016. USENIX Association.

[65]

Xiaowei Zhu, Wentao Han, and Wenguang Chen. Gridgraph: Large-scale graph processing on a single machine using 2-level hierarchical partitioning. In 2015 USENIX Annual Technical Conference (USENIX ATC 15), pages 375--386, Santa Clara, CA, 2015. USENIX Association.

Digital Library

Cited By

Song YWang KSun XQin ZDai HChen WLv BChen J(2025)A multi-source log semantic analysis-based attack investigation approachComputers & Security10.1016/j.cose.2024.104303(104303)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104303
Zhao YWang WChen SZhang BWang JWang Z(2024)Speeding Up Subgraph Matching Queries with Schema Guided IndexProceedings of the 2024 3rd International Conference on Networks, Communications and Information Technology10.1145/3672121.3672129(34-38)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3672121.3672129
Sharma AMehta DWu B(2024)Understanding High-Performance Subgraph Pattern Matching: A Systems PerspectiveProceedings of the 7th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3661304.3661897(1-12)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3661304.3661897
Show More Cited By

Index Terms

AutoMine: harmonizing high-level abstraction and high performance for graph mining
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
      1. Shared memory algorithms
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

Recommendations

SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems
MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are memory-...
Mining Frequent Subgraph Patterns from Uncertain Graph Data

In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph ...
Mining frequent cross-graph quasi-cliques

Joint mining of multiple datasets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in bioinformatics, jointly mining multiple gene expression datasets obtained by different ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SOSP '19: Proceedings of the 27th ACM Symposium on Operating Systems Principles

October 2019

615 pages

ISBN:9781450368735

DOI:10.1145/3341301

General Chairs:
Tim Brecht
University of Waterloo
,
Carey Williamson
University of Calgary

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGOPS: ACM Special Interest Group on Operating Systems

In-Cooperation

USENIX Assoc: USENIX Assoc

Publisher

Association for Computing Machinery

New York, NY, United States

Publication Notes

Badge change: Article originally badged under Version 1.0 guidelines https://www.acm.org/publications/policies/artifact-review-badging

Publication History

Published: 27 October 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

SOSP '19

Sponsor:

SIGOPS

SOSP '19: ACM SIGOPS 27th Symposium on Operating Systems Principles

October 27 - 30, 2019

Ontario, Huntsville, Canada

Acceptance Rates

Overall Acceptance Rate 174 of 961 submissions, 18%

Upcoming Conference

SOSP '25

Sponsor:
sigops

ACM SIGOPS 31st Symposium on Operating Systems Principles

October 13 - 16, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
2,903
Total Downloads

Downloads (Last 12 months)298
Downloads (Last 6 weeks)38

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Song YWang KSun XQin ZDai HChen WLv BChen J(2025)A multi-source log semantic analysis-based attack investigation approachComputers & Security10.1016/j.cose.2024.104303(104303)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104303
Zhao YWang WChen SZhang BWang JWang Z(2024)Speeding Up Subgraph Matching Queries with Schema Guided IndexProceedings of the 2024 3rd International Conference on Networks, Communications and Information Technology10.1145/3672121.3672129(34-38)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3672121.3672129
Sharma AMehta DWu B(2024)Understanding High-Performance Subgraph Pattern Matching: A Systems PerspectiveProceedings of the 7th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3661304.3661897(1-12)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3661304.3661897
Cai STian BZhang HGao M(2024)PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory HardwareProceedings of the ACM on Management of Data10.1145/36549642:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654964
Zhang ZLu YZheng WLin X(2024)A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and InteractionProceedings of the ACM on Management of Data10.1145/36393152:1(1-29)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639315
Yan DYuan LAhmad AZheng CChen HCheng JBaeza-Yates RBonchi F(2024)Systems for Scalable Graph Analytics and Machine Learning: Trends and MethodsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671472(6627-6632)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671472
Lin ZMeng KShui CZhang KXiao JTan GLee IChabbi MSteuwer M(2024)Exploiting Fine-Grained Redundancy in Set-Centric Graph Pattern MiningProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638507(175-187)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3627535.3638507
Fu TWei CWang YYing RAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)DeSCo: Towards Generalizable and Scalable Deep Subgraph CountingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635788(218-227)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635788
Li YSun SXiao HYe CLu SHe B(2024)A Survey on Concurrent Processing of Graph Analytical Queries: Systems and AlgorithmsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339393636:11(5508-5528)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3393936
Fu YHuang J(2024)TeMotif: An Efficient Approach to Temporal Motif Matching2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)10.1109/SDPC62810.2024.10707723(8-14)Online publication date: 26-Jul-2024
https://doi.org/10.1109/SDPC62810.2024.10707723
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents