A Parallel Network Community Detection Algorithm Based on Distance Dynamics
In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing fast. It is necessary to find an effective parallel community detection algorithm for large-scale network. In this paper, we propose a nove...
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| Abstract | In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing fast. It is necessary to find an effective parallel community detection algorithm for large-scale network. In this paper, we propose a novel and parallel community detection algorithm, PCDU algorithm, based on distance dynamics. We send distances information to nodes and update distances of edges constantly, based on previous values and the unified model, which is introduced to quantify different influences from nodes and edges. It ends until the distances are stable. Then we remove some special edges from the original graph and get all subgraphs, which are the community partitions. It still inherits the advantage of uncovering small communities and outliers. Experiments based on synthetic networks and real world networks, show that our algorithm execute more efficient than stand-alone version. Since it is based on the Spark platform and designed in parallelization, the algorithm is very suitable for large datasets. We also provide a novel method taking use of double summation to calculate the NMI value of community partition result and the embedded community structure. Compared with the traditional way, it is not only as accurate as the traditional way and more efficient, but also has less space complexity. Experiments show that it is suitable for evaluating community division results in large-scale network. |
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| AbstractList | In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing fast. It is necessary to find an effective parallel community detection algorithm for large-scale network. In this paper, we propose a novel and parallel community detection algorithm, PCDU algorithm, based on distance dynamics. We send distances information to nodes and update distances of edges constantly, based on previous values and the unified model, which is introduced to quantify different influences from nodes and edges. It ends until the distances are stable. Then we remove some special edges from the original graph and get all subgraphs, which are the community partitions. It still inherits the advantage of uncovering small communities and outliers. Experiments based on synthetic networks and real world networks, show that our algorithm execute more efficient than stand-alone version. Since it is based on the Spark platform and designed in parallelization, the algorithm is very suitable for large datasets. We also provide a novel method taking use of double summation to calculate the NMI value of community partition result and the embedded community structure. Compared with the traditional way, it is not only as accurate as the traditional way and more efficient, but also has less space complexity. Experiments show that it is suitable for evaluating community division results in large-scale network. |
| Author | Guo, Qian Wu, Bin Zhang, Cuiyun |
| Author_xml | – sequence: 1 givenname: Bin surname: Wu fullname: Wu, Bin email: wubin@bupt.edu organization: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 2 givenname: Cuiyun surname: Zhang fullname: Zhang, Cuiyun email: zhcuiyun@bupt.edu.cn organization: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 3 givenname: Qian surname: Guo fullname: Guo, Qian email: guoqian@bupt.edu.cn organization: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China |
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| Keywords | NMI parallelization community detection distance dynamics |
| Language | English |
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| SubjectTerms | community detection Computing methodologies Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Learning paradigms Computing methodologies -- Machine learning -- Learning paradigms -- Unsupervised learning distance dynamics Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social networks Information systems Information systems -- Data management systems Information systems -- Data management systems -- Database design and models Information systems -- Data management systems -- Database design and models -- Graph-based database models Information systems -- Information systems applications Information systems -- Information systems applications -- Data mining Information systems -- World Wide Web Information systems -- World Wide Web -- Web applications Information systems -- World Wide Web -- Web applications -- Social networks NMI parallelization Theory of computation Theory of computation -- Design and analysis of algorithms Theory of computation -- Design and analysis of algorithms -- Distributed algorithms Theory of computation -- Design and analysis of algorithms -- Distributed algorithms -- MapReduce algorithms Theory of computation -- Design and analysis of algorithms -- Graph algorithms analysis Theory of computation -- Design and analysis of algorithms -- Graph algorithms analysis -- Dynamic graph algorithms Theory of computation -- Theory and algorithms for application domains Theory of computation -- Theory and algorithms for application domains -- Machine learning theory Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Unsupervised learning and clustering |
| Title | A Parallel Network Community Detection Algorithm Based on Distance Dynamics |
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