Modeling and Simulation On Influence of Complex Network Nodes Based on Data Field in

doi:10.16182/j.issn1004731x.joss.18-0837

Abstract

Abstract: Research on the influence of complex network nodes is an important part of data mining. Mining the influential nodes in complex networks not only has important academic significance, but also helps to suppress the outbreak of epidemics, control the spread of rumors, and promote e-commercial products and so on. By selecting the Mixed Degree Decomposition (MDD) value of each node as its mass, the complex network is abstracted into a data field, the influential nodes are identified by combining the data field model, and some well-known centralities are compares with. The classical Susceptible-Infected-Recovered (SIR) epidemic model is used to evaluate the simulation performance by comparing the number of infected nodes. Simulations on real networks show that the data field can effectively identify the influential nodes.

Key words: complex networks, influential nodes, mixed degree decomposition, data field, simulation

CLC Number:

TP391.9

Shao Chenxi, Chen Xiaoqi, Wang Xingfu, Miao Fuyou. Modeling and Simulation On Influence of Complex Network Nodes Based on Data Field in[J]. Journal of System Simulation, 2020, 32(7): 1257-1266.

References 24

[1]	Wang S, Li Y, Wang D.Data field for mining big data[J]. Geo-spatial Information Science (S1009-5020), 2016, 19(2): 106-118.
[2]	Ma L L, Ma C, Zhang H F, et al.Identifying influential spreaders in complex networks based on gravity formula[J]. Physica A Statistical Mechanics & Its Applications (S0378-4371), 2015, 451: 205-212.
[3]	Tang Y, Li M, Wang J, et al.CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks[J]. Biosystems (S0303-2647), 2015, 127: 67-72.
[4]	Riondato M, Kornaropoulos E M.Fast approximation of betweenness centrality through sampling[J]. Data Mining and Knowledge Discovery (S1384-5810), 2016, 30(2): 438-475.
[5]	Solé-Ribalta A, De Domenico M, Gómez S, et al.Random walk centrality in interconnected multilayer networks[J]. Physica D: Nonlinear Phenomena (S0167-2789), 2016, 323: 73-79.
[6]	Kitsak M, Gallos L K, Havlin S, et al.Identification of influential spreaders in complex networks[J]. Nature Physics (S1745-2473), 2010, 6(11): 888-893.
[7]	Zeng A, Zhang C J.Ranking spreaders by decomposing complex networks[J]. Physics Letters A (S0375-9601), 2013, 377(14): 1031-1035.
[8]	Chen D, Lü L, Shang M S, et al.Identifying influential nodes in complex networks[J]. Physica a: Statistical mechanics and its applications (S0378-4371), 2012, 391(4): 1777-1787.
[9]	Lin J H, Guo Q, Dong W Z, et al.Identifying the node spreading influence with largest k-core values[J]. Physics Letters A (S0375-9601), 2014, 378(45): 3279-3284.
[10]	Zhuo Z, Zhang X, Niu W, et al.Improving data field hierarchical clustering using Barnes-Hut algorithm[J]. Pattern Recognition Letters (S0167-8655), 2016, 80(sep.1): 113-120.
[11]	Li T, Wang Y, Guan Z H.Spreading dynamics of a SIQRS epidemic model on scale-free networks[J]. Communications in Nonlinear Science and Numerical Simulation (S1007-5704), 2014, 19(3): 686-692.
[12]	Sun Z, Miao F, Xu Y, et al.Community detection in complex networks using flow simulation[C]// Proceedings of the 2017 VI International Conference on Network, Communication and Computing. Kunming, PEOPLES R CHINA: ACM, 2017: 150-155.
[13]	Bae J, Kim S.Identifying and ranking influential spreaders in complex networks by neighborhood coreness[J]. Physica A Statistical Mechanics & Its Applications (S0378-4371), 2014, 395(4): 549-559.
[14]	Barany I, Vu V.Central limit theorems for Gaussian polytopes[J]. The Annals of Probability (S0091-1798), 2007, 35(4): 1593-1621.
[15]	Wang S, Chen Y.HASTA: A Hierarchical-Grid Clustering Algorithm with Data Field[J]. International Journal of Data Warehousing & Mining (S1548-3924), 2014, 10(2): 39-54.
[16]	Liu Y, Jin J, Zhang Y, et al.A new clustering algorithm based on data field in complex networks[J]. The Journal of Supercomputing (S1573-0484), 2014, 67(3): 723-737.
[17]	Wang S, Wang S, Yuan H, et al.Clustering by differencing potential of data field[J]. Computing (S1436-5057), 2018, 100(4): 403-419.
[18]	Zachary W W.An information flow model for conflict and fission in small groups[J]. Journal of Anthropological Research (S0091-7710), 1977, 33(4): 452-473.
[19]	Lusseau D, Schneider K, Boisseau O J, et al.The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations[J]. Behavioral Ecology and Sociobiology (S0340-5443), 2003, 54(4): 396-405.
[20]	Huang Z, Wang Z, Zhang Z.Detecting Overlapping and Hierarchical Communities in Complex Network Based on Maximal Cliques[C]// Chinese National Conference on Social Media Processing. Guangzhou, PEOPLES R CHINA: Springer Singapore, 2015.
[21]	Karimi-Majd A M, Fathian M, Amiri B. A hybrid artificial immune network for detecting communities in complex networks[J]. Computing (S1436-5057), 2015, 97(5): 483-507.
[22]	Lancichinetti A, Fortunato S, Radicchi F.Benchmark graphs for testing community detection algorithms[J]. Physical review E (S2470-0053), 2008, 78(4): 046110.
[23]	Li C, Wang L, Sun S, et al.Identification of influential spreaders based on classified neighbors in real-world complex networks[J]. Applied Mathematics and Computation (S0096-3003), 2018, 320: 512-523.
[24]	Fu Y H, Huang C Y, Sun C T.Using global diversity and local features to identify influential social network spreaders[C]// IEEE/ACM International Conference on Advances in Social Networks Analysis & Mining. Beijing, PEOPLES R CHINA: IEEE, 2014.