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Clique Counting in MapReduce

Irene Finocchi, Marco Finocchi, Emanuele G. Fusco
2015 ACM Journal of Experimental Algorithmics  
We give both theoretical and experimental contributions. On the theory side, we design the first exact scalable algorithm for counting (and listing) k-cliques.  ...  Clique counting is essential in a variety of applications, among which social network analysis.  ...  In this paper we tackle the problem of counting the number of k-cliques in large-scale graphs, for any small constant k ≥ 3, designing exact and approximate MapReduce algorithms for this problem.  ... 
doi:10.1145/2794080 fatcat:hjibwlgr5za75dbcz3hymkavsy

Accelerating Clique Counting in Sparse Real-World Graphs via Communication-Reducing Optimizations [article]

Amogh Lonkar, Scott Beamer
2021 arXiv   pre-print
Different algorithms for clique counting avoid counting the same clique multiple times by pivoting or ordering the graph.  ...  Ordering-based algorithms include an ordering step to direct the edges in the input graph, and a counting step, which is dominated by building node or edge-induced subgraphs.  ...  [21] present a scalable algorithm for counting k-cliques using the MapReduce framework. They use a degree ordering to direct the graph. More recently, Shi et al.  ... 
arXiv:2112.10913v2 fatcat:fdhml5ywoza75bbhdxjoa4bfjq

MapReduce and Streaming Algorithms for Diversity Maximization in Metric Spaces of Bounded Doubling Dimension [article]

Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal
2017 arXiv   pre-print
In this work we present space and pass/round-efficient diversity maximization algorithms for the Streaming and MapReduce models and analyze their approximation guarantees for the relevant class of metric  ...  This improves substantially over the approximation ratios attainable in Streaming and MapReduce by state-of-the-art algorithms for general metric spaces.  ...  The work of Eli Upfal was supported in part by NSF grant IIS-1247581 and NIH grant R01-CA180776.  ... 
arXiv:1605.05590v4 fatcat:gcgzhwvizjgrpgvnuwu3ubvvua

Scalable subgraph counting using MapReduce

Ahmad Naser eddin, Pedro Ribeiro
2017 Proceedings of the Symposium on Applied Computing - SAC '17  
For that we present a dynamic iterative MapReduce strategy to parallelize algorithms that induce an unbalanced search tree, and apply it in the subgraph counting realm.  ...  Algorithmically, subgraph counting in a network is a computationally hard problem and the needed execution time grows exponentially as the size of the subgraph or the network increases.  ...  Acknowledgments This work is supported by ERDF through the COMPETE Programme within project POCI-01-0145-FEDER-006961, by national Funds through FCT within projects Reminds/ UTAP-ICDT/EEI-CTP/0022/2014 and  ... 
doi:10.1145/3019612.3019744 dblp:conf/sac/EddinR17 fatcat:w4cy2p6abnc57o4iuenmawvwo4

Scalable subgraph enumeration in MapReduce

Longbin Lai, Lu Qin, Xuemin Lin, Lijun Chang
2015 Proceedings of the VLDB Endowment  
We show that in the Erdös-Rényi random-graph model, TwinTwigJoin is instance optimal in the left-deep-join framework under reasonable assumptions, and we devise an algorithm to compute the optimal join  ...  Motivated by this, in this paper, we propose a new algorithm TwinTwigJoin based on a left-deep-join framework in MapReduce, in which the basic join unit is a TwinTwig (an edge or two incident edges of  ...  Xuemin Lin is supported by NSFC61232006, ARC DP120104168, ARC DP140103578, and ARC DP150102728. Lijun Chang is supported by ARC DE150100563.  ... 
doi:10.14778/2794367.2794368 fatcat:xo6iovkjajbxllwzftiue5akgy

MAPSkew: Metaheuristic Approaches for Partitioning Skew in MapReduce

Matheus Pericini, Lucas Leite, Francisco de Carvalho-Junior, Javam Machado, Cenez Rezende
2018 Algorithms  
Our experimental evaluation, using a MapReduce implementation of the Bron-Kerbosch Clique Algorithm, shows that the proposed method can find good partitionings while better balancing data among machines  ...  MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines.  ...  Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/a12010005 fatcat:tucxw5tidvev7bi6nvlw3ipiyi

Mining maximal cliques from a large graph using MapReduce: Tackling highly uneven subproblem sizes

Michael Svendsen, Arko Provo Mukherjee, Srikanta Tirthapura
2015 Journal of Parallel and Distributed Computing  
We implemented PECO on Hadoop MapReduce, and our experiments on a cluster show that the algorithm can effectively process a variety of large real-world graphs with millions of vertices and tens of millions  ...  We present a new parallel algorithm for MCE, Parallel Enumeration of Cliques using Ordering (PECO), designed for the MapReduce framework.  ...  Acknowledgments This work was funded in part by the National Science Foundation through grants 0834743 and 0831903, and through a gift from Northrop Grumman Corporation.  ... 
doi:10.1016/j.jpdc.2014.08.011 fatcat:zdjxdmwr6jf7fn3detaxkgfv3e

Efficient and Scalable Graph Similarity Joins in MapReduce

Yifan Chen, Xiang Zhao, Chuan Xiao, Weiming Zhang, Jiuyang Tang
2014 The Scientific World Journal  
The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results.  ...  Leveraging the MapReduce programming model, we proposeMGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins.  ...  Acknowledgments The research is supported by the doctoral program of higher education of China (No. 2011437110008) and the national natural science foundation of China (No. 61303062).  ... 
doi:10.1155/2014/749028 pmid:25121135 pmcid:PMC4121100 fatcat:w66vd5lfjbg5tfdmimlnsji4qu

An Efficient Parallel and Distributed Algorithm on Top of MapReduce

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
This article provides MapReduce-dependent calculation of a parallel mafia subspace bunching.  ...  The calculation exploits MapReduce's information apportioning in addition undertaking parallelism and accomplishes decent tradeoff amongst the expense for plate gets to besides correspondence fare.  ...  Below are the unique words taken from all documents and then put their count in below those words and if word not present in document then put 0.  ... 
doi:10.35940/ijitee.j9075.0881019 fatcat:d3ou3v62jjdizgvni6bcuyvku4

Enumerating Maximal Bicliques from a Large Graph Using MapReduce

Arko Provo Mukherjee, Srikanta Tirthapura
2014 2014 IEEE International Congress on Big Data  
We present novel parallel algorithms for the MapReduce framework, and an experimental evaluation using Hadoop MapReduce.  ...  We consider the enumeration of maximal bipartite cliques (bicliques) from a large graph, a task central to many data mining problems arising in social network analysis and bioinformatics.  ...  Our Algorithm is described in Algorithm 13. Algorithms 14 and 15 (map and reduce) describe the consensus operation using MapReduce.  ... 
doi:10.1109/bigdata.congress.2014.105 dblp:conf/bigdata/MukherjeeT14 fatcat:q5upqincsjarxdb3adoj4w2vaa

Scalable big graph processing in MapReduce

Lu Qin, Jeffrey Xu Yu, Lijun Chang, Hong Cheng, Chengqi Zhang, Xuemin Lin
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
In the literature, there are MapReduce Class MRC and Minimal MapReduce Class MMC to define the memory consumption, communication cost, CPU cost, and number of MapReduce rounds for an algorithm to execute  ...  We believe our findings on MapReduce can also guide the development of scalable graph processing algorithms in other systems in cloud.  ...  Graph Processing in MapReduce: Many graph algorithms including triangles/rectangles enumeration, k-cliques computation, barycentric clustering, and components finding in MapReduce are discussed in [9]  ... 
doi:10.1145/2588555.2593661 dblp:conf/sigmod/QinYCCZL14 fatcat:7y2bdsuxvnajrb3vhcvqo2ilnq

Enumerating Maximal Bicliques from a Large Graph Using MapReduce

Arko Provo Mukherjee, Srikanta Tirthapura
2017 IEEE Transactions on Services Computing  
We present novel parallel algorithms for the MapReduce framework, and an experimental evaluation using Hadoop MapReduce.  ...  We consider the enumeration of maximal bipartite cliques (bicliques) from a large graph, a task central to many data mining problems arising in social network analysis and bioinformatics.  ...  Our Algorithm is described in Algorithm 13. Algorithms 14 and 15 (map and reduce) describe the consensus operation using MapReduce.  ... 
doi:10.1109/tsc.2016.2523997 fatcat:kivpaowf3faf7pi5zhvl45btdm

MapReduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension

Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal
2017 Proceedings of the VLDB Endowment  
In this work we present space and pass/round-efficient diversity maximization algorithms for the Streaming and MapReduce models and analyze their approximation guarantees for the relevant class of metric  ...  This improves substantially over the approximation ratios attainable in Streaming and MapReduce by state-ofthe-art algorithms for general metric spaces.  ...  Our algorithms require only one pass over the data, in the streaming setting, and only two rounds in MapReduce.  ... 
doi:10.14778/3055540.3055541 fatcat:2w6lms7vi5hs7ekiivf757w4pi

MR-ECOCD: An Edge Clustering Algorithm for Overlapping Community Detection on Large-Scale Network Using MapReduce

Haitao He, Peng Zhang, Jun Dong, Jiadong Ren
2016 International Journal of Innovative Computing, Information and Control  
Extensive experiments show that our algorithm can effectively and fast detect overlapping communities.  ...  MR-ECOCD consists of four major stages, and all operations are executed in parallel using MapReduce.  ...  F2014203152 and No. F2015203326. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  ... 
doi:10.24507/ijicic.12.01.263 fatcat:p3vs5vmx4bfhlheeut2tmvcgt4

Distributed Gaussian Mixture Model Summarization Using the MapReduce Framework [chapter]

Arina Esmaeilpour, Elnaz Bigdeli, Fatemeh Cheraghchi, Bijan Raahemi, Behrouz H. Far
2016 Lecture Notes in Computer Science  
In this thesis, this goal is achieved by proposing and implementing a distributed Gaussian Mixture Model Summarization using the MapReduce framework (MR-SGMM).  ...  The main purpose of the proposed method is to summarize a dataset with a density-based clustering algorithm called DBSCAN algorithm, and then summarize each discovered cluster using the SGMM approach in  ...  CLIQUE (CLustering In QUEst) [41] is an algorithm with three steps.  ... 
doi:10.1007/978-3-319-34111-8_39 fatcat:ykoufaup7fht7mmkrwuhihpzhq
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