A big graph clustering method to support parallel processing by perceiving graph’s application algorithm semantics

As the size of graph data grows exponentially, it is usually processed by parallel and distributed computing environment. During parallel processing, the first step is to divide the graph data into many subgraphs and then place them on different computational nodes. However, the existing graph clust...

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Vydané v:	The Journal of supercomputing Ročník 80; číslo 2; s. 2838 - 2861
Hlavní autori:	Cheng, Tengteng, Zeng, Guosun, Sun, Zhipeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.01.2024 Springer Nature B.V
Predmet:	Algorithms Clustering Communication Compilers Computer Science Distributed processing Graph theory Interpreters Methods Modularity Parallel processing Processor Architectures Programming Languages Semantics Social networks Graph clustering Parallel processing Semantic serial degree Big graph Modularity of a subgraph Graph’s application algorithm
ISSN:	0920-8542, 1573-0484
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Abstract	As the size of graph data grows exponentially, it is usually processed by parallel and distributed computing environment. During parallel processing, the first step is to divide the graph data into many subgraphs and then place them on different computational nodes. However, the existing graph clustering methods cannot adapt to parallel processing or improve computing performance. The main reason is that these methods only focus on the graph data but do not care about the graph’s application algorithm. Therefore, this paper addresses a graph clustering method that supports parallel processing by perceiving the application algorithm semantics. We specifically focus on the characteristic of "tight inside and loose outside". Based on this characteristic, we give a new formula for calculating the modularity of the subgraph. In a graph application, graph data and its associated application algorithm are not separated from each other. Thus, we also focus on the execution patterns of the application algorithm. Combining the modularity of a subgraph and execution patterns of an application algorithm, we present the critical concept of semantic serial degree as a new criterion for big graph clustering. Consequently, we propose a graph clustering method that effectively balances the importance between the graph data and its associated application algorithm. By achieving this balance, our approach ensures that the final clustering results are more suitable for parallel and distributed processing. Extended experiments show that our proposed graph clustering method is more general and compatible with traditional clustering methods. Compared with FastUnfolding, a state-of-the-art graph clustering method, the completion times of an application algorithm are significantly reduced due to the help of the proposed graph clustering method.
AbstractList	As the size of graph data grows exponentially, it is usually processed by parallel and distributed computing environment. During parallel processing, the first step is to divide the graph data into many subgraphs and then place them on different computational nodes. However, the existing graph clustering methods cannot adapt to parallel processing or improve computing performance. The main reason is that these methods only focus on the graph data but do not care about the graph’s application algorithm. Therefore, this paper addresses a graph clustering method that supports parallel processing by perceiving the application algorithm semantics. We specifically focus on the characteristic of "tight inside and loose outside". Based on this characteristic, we give a new formula for calculating the modularity of the subgraph. In a graph application, graph data and its associated application algorithm are not separated from each other. Thus, we also focus on the execution patterns of the application algorithm. Combining the modularity of a subgraph and execution patterns of an application algorithm, we present the critical concept of semantic serial degree as a new criterion for big graph clustering. Consequently, we propose a graph clustering method that effectively balances the importance between the graph data and its associated application algorithm. By achieving this balance, our approach ensures that the final clustering results are more suitable for parallel and distributed processing. Extended experiments show that our proposed graph clustering method is more general and compatible with traditional clustering methods. Compared with FastUnfolding, a state-of-the-art graph clustering method, the completion times of an application algorithm are significantly reduced due to the help of the proposed graph clustering method.
Author	Cheng, Tengteng Zeng, Guosun Sun, Zhipeng
Author_xml	– sequence: 1 givenname: Tengteng surname: Cheng fullname: Cheng, Tengteng organization: Department of Computer Science and Technology, Tongji University, Tongji Branch, National Engineering and Technology Center of High-Performance Computer – sequence: 2 givenname: Guosun surname: Zeng fullname: Zeng, Guosun email: gszeng@tongji.edu.cn organization: Department of Computer Science and Technology, Tongji University, Tongji Branch, National Engineering and Technology Center of High-Performance Computer – sequence: 3 givenname: Zhipeng surname: Sun fullname: Sun, Zhipeng organization: Department of Computer Science and Technology, Tongji University, Tongji Branch, National Engineering and Technology Center of High-Performance Computer
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In: IEEE high performance extreme computing conference, p 1–6. https://doi.org/10.1109/HPEC.2019.8916299 – reference: NewmanMEGirvanMFinding and evaluating community structure in networksPhys Rev E200469211610.1103/PhysRevE.69.026113 – reference: ZubaroğluAAtalayVData stream clustering: a reviewArtif Intell Rev20215421201123610.1145/2522968.2522981 – reference: BarthelemyMBetweenness centrality in large complex networksEur Phys J B200438216316810.1140/epjb/e2004-00111-4 – reference: HuKZengGDingSCluster-scheduling big graph traversal task for parallel processing in heterogeneous cloud based on DAG transformationIEEE Access20197770707708210.1109/ACCESS.2019.2921477 – reference: JavedMAYounisMSLatifSCommunity detection in networks: a multidisciplinary reviewJ Netw Comput Appl20181088711110.1016/j.jnca.2018.02.011 – reference: LiTZhangYFast compressive spectral clustering for large-scale sparse graphIEEE Trans Big Data20228119320210.1109/TBDATA.2019.2931532 – reference: Rattigan MJ, Maier M et al (2007) Graph clustering with network structure indices. 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Title	A big graph clustering method to support parallel processing by perceiving graph’s application algorithm semantics
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