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Information is spread as a kind of energy in the network, and it has the ability to spread to nodes that go beyond the neighbors, that is, the information has a radiation effect. However, most of the studies of information dissemination in complex networks only consider the dissemination between neighbors, ignoring that their neighborhood will also be affected by the information radiation. According to this, we propose a new information radiation model with the ability to communicate across neighbors. Firstly, the concepts of information radiation range and radiation attenuation are put forward by combining the theory of complex network and the radiation theory. Secondly, by proposing the hypotheses and analyzing the information content, the nodes in the network are divided into three states:the radiation state, the known state, and the unknown state with the information amount serving as the criterion. At the same time, the transition rules between node states are defined. Thirdly, a three-layer information radiation network model is established based on the physical layer serving as the network structure, the radiation layer as the information dissemination environment, and the state layer as the radiation state statistics. Then, on the basis of the model, the differential equations of the state changes of the nodes are constructed by using the mean field theory and defining the network statistic such as the nth degree, the average nth degree and the nth degree distribution. By analyzing the mechanism of information radiation, the expression of information radiation threshold is deduced by using the differential equation set. Afterwards, the existence of information radiation threshold is proved in each of NW network, BA network, Jazz network, Net-science network, and E-mail network. And the results of numerical simulation and theoretical analysis are well fitted, verifying the correctness of theoretical analysis and the validity of the model. Finally, considering the practical situation of the application, the influences of the state transition probability and the radiation attenuation on the information radiation are analyzed in the BA network by using computer simulation. The results show that the radiation attenuation can stabilize the radiation, and the number of nodes in the initial state of radiation can be increased, which will accelerate the demise of the unknown state nodes but will not increase the number of nodes in the steady state. The results show that increasing the attenuation of the radiation can not only increase the number of radiation nodes in steady stage of radiation, but also speed up the demise of unknown state nodes. And increasing the state transition probability or will affect only the number of the radiation nodes in the initial stage of radiation, also accelerate the demise of the unknown state nodes but will not increase the number of radiation nodes in steady stage of radiation. The analyses of the state transition probability between nodes and the radiation attenuation also prove the correctness of the theoretical analysis.
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Keywords:
- spread abilities /
- information radiation /
- complex network /
- radiation threshold
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[1] Valerio A, Marco C, Massimiliano L G, Andrea P, Fabio P 2016 Comput. Commun. 76 26
[2] Fu R, Alexander G, Margaret L B 2016 Math. Biosci. 273 102
[3] Kang H, Fu X 2015 Commun. Nonlinear Sci. Numer. Simulat. 27 30
[4] He X S, Zhou M Y, Zhuo Z, Fu Z Q, Liu J G 2015 Physica A 436 658
[5] Yang L X, Yang X 2014 Physica A 396 173
[6] Wang Q, Lin Z, Jin Y, Cheng S, Yang T 2015 Knowledge-Based Systems 81 46
[7] Wang J P, Guo Q, Yang G Y, Liu J G 2015 Physica A 428 250
[8] Xiao Y, Han J 2016 Technological Forecasting Social Change 105 167
[9] Duncan A J, Gunn G J, Umstatter C, Humphry R W 2014 Theor. Popul. Biol. 98 11
[10] Ha J, Kim S W, Kim S W, Faloutsos C, Park S 2015 Inform. Sci. 290 45
[11] Nandi A K, Medal H R 2016 Comput. Oper. Res. 69 10
[12] mit A 2015 J. Magn. Magn. Mater. 386 60
[13] Hou L, Lao S, Small M, Xiao Y 2015 Phys. Lett. A 379 1321
[14] Liu C, Zhou L, Fan C, Huo L, Tian Z 2015 Physica A 432 269
[15] Zhang H F 2015 Ph. D. Dissertation (Beijing:Beijing Jiaotong University) (in Chinese)[张海峰2015博士学位论文(北京:北京交通大学)]
[16] Wang Y Q, Wang J, Yang H B 2014 Acta Phys. Sin. 63 208902 (in Chinese)[王亚奇, 王静, 杨海滨2014物理学报63 208902]
[17] Wu T F, Zhou C L, Wang X H, Huang X X, Zhan Z Q, Wang R B 2014 Acta Phys. Sin. 63 240501 (in Chinese)[吴腾飞, 周昌乐, 王小华, 黄孝喜, 谌志群, 王荣波2014物理学报63 240501]
[18] Yu Z, Wang C, Bu J, Wang X, Wu Y, Chen C 2015 Inform. Sci. 309 102
[19] Luo S, Du Y, Liu P, Xuan Z, Wang Y 2015 Expert Syst. Appl. 42 3619
[20] Yi T, Zhu Q X 2014 J. Loss Prevent. Process Ind. 27 130
[21] Wang J, Zhao L, Huang R 2014 Physica A 398 43
[22] Qiu X, Yu L, Zhang D 2015 Neuro Computing 155 247
[23] Choi C W, Xu C, Hui P M 2015 Phys. Lett. A 379 3029
[24] Arkadiusz J, Katarzyna S W, Janusz S 2016 Physica A 446 110
[25] Albert L, Barabasi, Albert R, Jeong H 1999 Physica A 272 173
[26] Li F 2015 J. North Univ. China (Nat. Sci. Ed.) 36 97(in Chinese)[李峰2015中北大学学报(自然科学版) 36 97]
[27] Chen W Y, Jia Z, Zhu G H 2015 J. Univ. Electron. Sci. Technol. China 44 172(in Chinese)[陈玟宇, 贾贞, 祝光湖2015电子科技大学学报44 172]
[28] Qian Z, Tang S, Zhang X, Zheng Z 2015 Physica A 429 95
[29] Li X, Cao L 2016 Physica A 450 624
[30] Xia C, Wang Z, Joaquin S, Sandro M, Yamir M 2013 Physica A 392 1577
[31] Song Y, Jiang G, Gong Y 2013 Chin. Phys. B 22 040205
[32] Li D F, Cao T G, Geng J P, Zhan Y 2015 Acta Phys. Sin. 64 248701 (in Chinese)[李多芳, 曹天光, 耿金鹏, 展永2015物理学报64 248701]
[33] Wang X Y, Wang Y, Zhu L, Li C 2016 Physica A 452 94
[34] Wang X Y, Wang Y, Zhu L 2016 J. Harbin Inst. Technol. 48 166 (in Chinese)[汪筱阳, 王瑛, 朱琳2016哈尔滨工业大学学报48 166]
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