Fast Markov Clustering Algorithm Based on Belief Dynamics
Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we pre...
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| Vydáno v: | IEEE transactions on cybernetics Ročník 53; číslo 6; s. 3716 - 3725 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
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| Abstract | Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches <inline-formula> <tex-math notation="LaTeX">O(TN) </tex-math></inline-formula> in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods. |
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| AbstractList | Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches <inline-formula> <tex-math notation="LaTeX">O(TN) </tex-math></inline-formula> in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods. Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches O(TN) in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods.Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches O(TN) in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods. Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes’ normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches [Formula Omitted] in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods. Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches O(TN) in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods. |
| Author | Xu, Wenzhe Qiu, Chenyang Li, Huijia Pei, Jian |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35077385$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1103/PhysRevE.69.066133 10.1103/PhysRevLett.110.148701 10.1103/PhysRevLett.100.258701 10.1109/TKDE.2019.2958806 10.1109/TCYB.2018.2871673 10.1145/347057.347412 10.1007/s10732-008-9080-4 10.1109/TKDE.2021.3104155 10.1073/pnas.0400054101 10.1016/j.physa.2010.01.042 10.1103/PhysRevE.84.046102 10.1109/ICNC.2014.6975883 10.1016/j.physa.2013.07.021 10.1088/1742-5468/2008/10/P10008 10.1145/3341161.3342890 10.1609/aaai.v29i1.9201 10.1103/PhysRevE.76.036106 10.1103/PhysRevE.69.026113 10.1073/pnas.0406024101 10.1016/j.physa.2012.03.021 10.1073/pnas.122653799 10.1103/PhysRevE.83.016107 10.1109/TEVC.2011.2161090 10.1609/aaai.v33i01.3301152 10.1109/TKDE.2010.233 10.1145/3269206.3271697 10.1103/PhysRevE.78.046110 10.1103/PhysRevE.90.012811 10.1109/TKDE.2019.2937298 10.1109/TCYB.2014.2377154 10.1145/1854776.1854812 10.1016/j.physa.2006.07.023 10.1126/science.1073374 10.1103/PhysRevE.70.066111 |
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| References | ref35 bishop (ref23) 2006; 128 ref12 ref34 ref15 ref37 ref36 van dongen (ref14) 2000 ref31 ref30 ref11 ref33 ref10 ref32 girvan (ref2) 2002; 99 reddy (ref5) 2002 ref1 ref17 ref16 ref38 ref19 ref18 pinto (ref13) 2016 ref24 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 |
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| SubjectTerms | Algorithms Belief dynamics Broadcasting Clustering Clustering algorithms complex networks Computational complexity Configurations Convergence Dynamics Heuristic algorithms large-scale networks Limit states Markov clustering algorithm Markov processes Networks Trajectory |
| Title | Fast Markov Clustering Algorithm Based on Belief Dynamics |
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