ScaleMine: Scalable Parallel Frequent Subgraph Mining in a Single Large Graph

被引：0
|
作者：
Abdelhamid, Ehab [1 ]
Abdelaziz, Ibrahim [1 ]
Kalnis, Panos [1 ]
Khayyat, Zuhair [1 ]
Jamour, Fuad [1 ]
机构：
[1] KAUST, Comp Elect & Math Sci & Engn Div, Extreme Comp Res Ctr, Thuwal, Saudi Arabia
来源：
SC '16: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS | 2016年
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This artifact contains the source codes of ScaleMine as well as the scripts required for compiling and running it. ScaleMine is a parallel frequent subgraph mining system that is capable of running on a single machine, a cluster of machines or a supercomputer. It assumes that the input graph is formatted using the widely used Labeled Graph (LG) format. ScaleMine implements all the algorithms described in the paper: ScaleMine: Scalable Parallel Frequent Subgraph Mining in a Single Large Graph, SC16. To validate the results, compile the source code, run the test scripts and check the results in the corresponding output files.
引用
收藏
页码：727 / 727
页数：1
相关论文
共 50 条
  • [21] Large-Scale Frequent Subgraph Mining in MapReduce
    Lin, Wenqing
    Xiao, Xiaokui
    Ghinita, Gabriel
    2014 IEEE 30TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2014, : 844 - 855
  • [22] Efficient frequent subgraph mining on large streaming graphs
    Ray, Abhik
    Holder, Lawrence B.
    Bifet, Albert
    INTELLIGENT DATA ANALYSIS, 2019, 23 (01) : 103 - 132
  • [23] An efficient and scalable approach for mining subgraphs in a single large graph
    Lam B. Q. Nguyen
    Loan T. T. Nguyen
    Bay Vo
    Ivan Zelinka
    Jerry Chun-Wei Lin
    Unil Yun
    Hung Son Nguyen
    Applied Intelligence, 2022, 52 : 17881 - 17895
  • [24] An efficient and scalable approach for mining subgraphs in a single large graph
    Nguyen, Lam B. Q.
    Nguyen, Loan T. T.
    Vo, Bay
    Zelinka, Ivan
    Lin, Jerry Chun-Wei
    Yun, Unil
    Nguyen, Hung Son
    APPLIED INTELLIGENCE, 2022, 52 (15) : 17881 - 17895
  • [25] Fast and scalable algorithms for mining subgraphs in a single large graph
    Nguyen, Lam B. Q.
    Bay Vo
    Ngoc-Thao Le
    Snasel, Vaclav
    Zelinka, Ivan
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 90
  • [26] Fast and scalable algorithms for mining subgraphs in a single large graph
    Nguyen, Lam B.Q.
    Vo, Bay
    Le, Ngoc-Thao
    Snasel, Vaclav
    Zelinka, Ivan
    Vo, Bay (vd.bay@hutech.edu.vn), 1600, Elsevier Ltd (90):
  • [27] Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database
    Priyadarshini, Sadhana
    Rodda, Sireesha
    SMART INTELLIGENT COMPUTING AND APPLICATIONS, VOL 2, 2020, 160 : 233 - 244
  • [28] A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns
    Rehman, Saif Ur
    Liu, Kexing
    Ali, Tariq
    Nawaz, Asif
    Fong, Simon James
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2021, 14 (01)
  • [29] Multi-graph Frequent Approximate Subgraph Mining for Image Clustering
    Acosta-Mendoza, Niusvel
    Ariel Carrasco-Ochoa, Jesus
    Gago-Alonso, Andres
    Francisco Martinez-Trinidad, Jose
    Eladio Medina-Pagola, Jose
    PROGRESS IN ARTIFICIAL INTELLIGENCE AND PATTERN RECOGNITION, IWAIPR 2018, 2018, 11047 : 133 - 140
  • [30] CCGraMi: An Effective Method for Mining Frequent Subgraphs in a Single Large Graph
    Nguyen L.B.Q.
    Zelinka I.
    Diep Q.B.
    Mendel, 2021, 27 (02) : 90 - 99
12345