基于人类动力学的在线社交网络信息传播研究

doi:10.11999/JEIT160940

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Abstract

In the Web2.0 era, the online social network has become an important carrier of social relationship maintenance and information dissemination in the human society because of its interactivity and instant. Therefore, it is very important to understand the behavior characteristics of social network users and its impact on online information dissemination. From the perspective of human behavior dynamics, the empirical research on the behavior of social network users in recent years is systematically reviewed. Secondly, the influence of social network user,s behavior on online information dissemination is summarized. Finally, the online social network information dissemination based on user behavior dynamics is summarized and prospected.

Key words： Online social network Human dynamics Information spreading Temporal heterogeneity

Received: 19 September 2016 Published: 07 March 2017

PACS:	TP393
	O414.2

Fund:

The National 973 Program of China (2013CB329604), The National Natural Science Foundation of China (71601005, 71231002)

Corresponding Authors: WU Lianren E-mail: lianrenwu@suibe.edu.cn

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	LI Jinjie
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	QI Jiayin
	YAN Qiang

Cite this article:

LI Jinjie,WU Lianren,QI Jiayin等. Research on Information Dissemination in Online Social Network Based on Human Dynamics[J]. JEIT, 2017, 39(4): 785-793.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT160940 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I4/785

[1]	方滨兴, 贾焰, 韩毅. 社交网络分析核心科学问题, 研究现状及未来展望[J]. 中国科学院院刊, 2015, 30(2): 187-199. doi: 10. 16418/j.issn.1000-3045.2015.02.007.
	FANG Binxing, JIA Yan, and HAN Yi. Social network analysis-key research problems, related work, and future prospects[J]. Bulletin of Chinese Academy of Sciences, 2015, 30(2): 187-199. doi: 10.16418/j.issn.1000-3045.2015.02.007.
[2]	杨善林, 王佳佳, 代宝, 等. 在线社交网络用户行为研究现状与展望[J]. 中国科学院院刊, 2015, 30(2): 200-215. doi: 10.16418 /j.issn.1000-3045.2015.02.008.
	YANG Shanlin, WANG Jiajia, DAI Bao, et al. State of the art in social network user behaviors and its future[J]. Bulletin of Chinese Academy of Sciences, 2015, 30(2): 200-215. doi: 10.16418/j.issn.1000-3045.2015.02.008.
[3]	DEZSO Z, ALMAAS E, LUKACS A, et al. Dynamics of information access on the web[J]. Physical Review E, 2006, 73(6): 066132. doi: 10.1103/PhysRevE.73.066132.
[4]	GONCALVES B and RAMASCO J J. Human dynamics revealed through Web analytics[J]. Physical Review E, 2008, 78(2): 026123. doi: 10.1103/PhysRevE.78.026123.
[5]	ZHAO Z D, XIA H, SHANG M S, et al. Empirical analysis on the human dynamics of a large-scale short message communication system[J]. Chinese Physics Letters, 2011, 28(6): 068901. doi: 10.1088/0256-307X/28/6/068901.
[6]	HU H B and HAN D Y. Empirical analysis of individual popularity and activity on an online music service system[J]. Physica A: Statistical Mechanics and Its Applications, 2008, 387(23): 5916-5921. doi: http: //dx.doi.org/10.1016/j.physa. 2008.06.018.
[7]	周涛, 韩筱璞, 闫小勇, 等. 人类行为时空特性的统计力学[J]. 电子科技大学学报, 2013, 42(4): 481-540. doi: 10.3969/j.issn. 1001-0548.2013.04.001.
	ZHOU Tao, HAN Xiaopu, YAN Xiaoyong, et al. Statistical mechanics on temporal and spatial activities of human[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(4): 481-540. doi: 10.3969/j.issn.1001-0548. 2013.04.001.
[8]	BARABASI A L. The origin of bursts and heavy tails in human dynamics[J]. Nature, 2005(435): 207-211. doi: 10.1038/nature03459.
[9]	BROCKMANN D, HUFNAGEL L, and GEISEL T. The scaling laws of human travel[J]. Nature, 2006(439): 462-465. doi: 10.1038/nature04292.
[10]	WU Y, ZHOU C, XIAO J, et al. Evidence for a bimodal distribution in human communication[J]. Proceedings of the National Academy of Sciences, 2010, 107(44): 18803-18808. doi: 10.1073/pnas.1013140107.
[11]	WANG P, GONZALEZ M C, HIDALFO C A, et al. Understanding the spreading patterns of mobile phone viruses[J]. Science, 2009(324): 1071-1076. doi: 10.1126/ science.1167053.
[12]	DEVILLE P, SONG C, EAGLE N, et al. Scaling identity connects human mobility and social interactions[J]. Proceedings of the National Academy of Sciences, 2016, 113(26): 7047-7052. doi: 10.1073/pnas.1525443113.
[13]	YAN Q, WU L R, and ZHENG L. Social network based microblog user behavior analysis[J]. Physica A: Statistical Mechanics and Its Applications, 2013, 392(7): 1712-1723. doi: http://dx.doi.org/10.1016/j.physa.2012.12.008.
[14]	YAN Q, YI L L, and WU L R. Human dynamic model co-driven by interest and social identity in the MicroBlog community[J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(4): 1540-1545. doi: http://dx.doi.org /10.1016/j.physa.2011.08.038.
[15]	闫强, 吴联仁, 郑兰. 微博社区中用户行为特征及其机理研究[J]. 电子科技大学学报, 2013, 42(3): 328-333. doi: 10.3969/ j.issn.1001-0548.2013.03.002.
	YAN Q, WU L R, and ZHENG L. Research on user behavior characters and mechanism in micro-blog communities[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(3): 328-333. doi: 10.3969/j.issn.1001-0548. 2013.03.002.
[16]	CHEN G, HAN X, and WANG B. Multi-level scaling properties of instant-message communications[J]. Physics Procedia, 2010, 3(5): 1897-1905. doi: 10.1016/j.phpro.2010. 07.034.
[17]	WANG P, ZHOU T, HAN X P, et al. Modeling correlated human dynamics[OL]. http://arXiv preprint arXiv:1007.4440, 2010.
[18]	RADICCHI F. Human activity in the web[J]. Physical Review E, 2009, 80(2): 026118. doi: 10.1103/PhysRevE.80.026118.
[19]	LESKOVEC J and HORVITZ E. Planetary-scale views on a large instant-messaging network[C]. Proceedings of the 17th International Conference on World Wide Web, ACM, USA, 2008: 915-924. doi: 10.1145/1367497.1367620.
[20]	GOETZ M, LESKOVEC J, MCGLOHON M, et al. Modeling blog dynamics[C]. Third International AAAI Conference on Weblogs and Social Media, California, USA, 2009: 1-8.
[21]	OLIVEIRA J G and BARABASI A L. Human dynamics: Darwin and Einstein correspondence patterns[J]. Nature, 2005, 437: 1251-1251. doi: 10.1038/4371251a.
[22]	MASUDA N, KIM J S, and KAHNG B. Priority queues with bursty arrivals of incoming tasks[J]. Physical Review E, 2009, 79(3): 036106. doi: 10.1103/PhysRevE.79.036106.
[23]	CAJUEIRO D O and MALDONADO W L. Role of optimization in the human dynamics of task execution[J]. Physical Review E, 2008, 77(3): 035101. doi: 10.1103/ PhysRevE.77.035101.
[24]	OLIVEIRA J G and VAZQUEZ A. Impact of interactions on human dynamics[J]. Physica A: Statistical Mechanics and Its Applications, 2009, 388(2/3): 187-192. doi: http: //dx.doi. org/10.1016/j.physa. 2008.08.022.
[25]	MIN B, GOH K I, and KIM I M. Waiting time dynamics of priority-queue networks[J]. Physical Review E, 2009, 79(5): 056110. doi: 10.1103/PhysRevE.79.056110.
[26]	VAZQUEZ A. Impact of memory on human dynamics[J]. Physica A: Statistical Mechanics and Its Applications, 2007, 373: 747-752. doi: http: //dx.doi.org/10.1016/j.physa.2006. 04.060.
[27]	HAN X P, ZHOU T, and WANG B H. Modeling human dynamics with adaptive interest[J]. New Journal of Physics, 2008, 10(7): 073010. doi: 10.1088/1367-2630/10/7/073010.
[28]	SHANG M S, CHEN G X, DAI S X, et al. Interest-driven model for human dynamics[J]. Chinese Physics Letters, 2010(27): 048701. doi: 10.1088/0256-307X/27/4/048701.
[29]	HIDALGO R C A. Conditions for the emergence of scaling in the inter-event time of uncorrelated and seasonal systems[J]. Physica A: Statistical Mechanics and Its Applications, 2006, 369(2): 877-883. doi: http: //dx.doi.org/10.1016/j.physa. 2005.12.035.
[30]	韩佳, 肖如良, 胡耀, 等. 在线社交网络中信息传播模式的特征分析[J]. 计算机应用, 2013, 33(1): 105-107. doi: 10. 3724/SP.J.1087.2013.00105.
	HAN J, XIAO R L, HU Y, et al. Characteristic analysis of information propagation pattern in online social network[J]. Journal of Computer Applications, 2013, 33(1): 105-107. doi: 10.3724/SP.J.1087.2013.00105.
[31]	张赛, 徐恪, 李海涛. 微博类社交网络中信息传播的测量与分析[J]. 西安交通大学学报, 2013, 47(2): 124-129. doi: 10.7652/ xjtuxb201302021.
	ZHANG S, XU K, and LI H. Measurement and analysis of information propagation in online social networks like micro-blog[J]. Journal of Xi’an Jiaotong University, 2013, 47(2): 124-129. doi: 10.7652/xjtuxb201302021.
[32]	李洋, 陈毅恒, 刘挺. 微博信息传播预测研究综述[J]. 软件学报, 2016, 27(2): 247-263. doi: 10.13328/j.cnki.jos.004944.
	LI Y, CHEN Y H, and LIU T. Survey on predicting information propagation in microblogs[J]. Journal of Software, 2016, 27(2): 247-263. doi: 10.13328/j.cnki.jos.004944.
[33]	CENTOLA D. The spread of behavior in an online social network experiment[J]. Science, 2010, 329(5996): 1194-1197. doi: 10.1126/science.1185231.
[34]	GUILLE A, HACID H, FAVRE C, et al. Information diffusion in online social networks: A survey[J]. ACM SIGMOD Record, 2013, 42(2): 17-28. doi: 10.1145/2503792. 2503797.
[35]	KARSAI M, KIVELA M, PAN R K, et al. Small but slow world: How network topology and burstiness slow down spreading[J]. Physical Review E, 2011, 83(2), 025102. doi: 10.1103/PhysRevE.83.025102.
[36]	ZHAO L, WANG J, CHEN Y, et al. SIHR rumor spreading model in social networks[J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(7): 2444-2453. doi: http: // dox.doi.org/10.1016/j.physa.2011.12.008.
[37]	ZHAO L, CUI H, QIU X, et al. SIR rumor spreading model in the new media age[J]. Physica A: Statistical Mechanics and Its Applications, 2013, 392(4): 995-1003. doi: http: //dox. doi.org/10.1016/j.physa. 2012.09.030.
[38]	LU L, CHEN D B, and ZHOU T. The small world yields the most effective information spreading[J]. New Journal of Physics, 2011, 13(12): 123005. doi: 10.1088/1367-2630/13/ 12/123005.
[39]	ZHENG M, LU L, and ZHAO M. Spreading in online social networks: The role of social reinforcement[J]. Physical Review E, 2013, 88(1): 012818. doi: 10.1103/PhysRevE.88.012818.
[40]	ZHOU T, LIU J G, BAI W J, et al. Behaviors of susceptible- infected epidemics on scale-free networks with identical infectivity[J]. Physical Review E, 2006, 74(5): 056109. doi: 10.1103/PhysRevE.74.056109.
[41]	朱张祥, 刘咏梅. 在线社交网络谣言传播的仿真研究基于聚类系数可变的无标度网络环境[J]. 复杂系统与复杂性科学, 2016, 13(2): 74-82. doi: 10.13306/j.1672-3813.2016.02. 009.
	ZHU Z X and LIU Y M. Simulation study of propagation of rumor in online social network based on scale-free network with tunable clustering[J]. Complex Systems and Complexity Science, 2016, 13(2): 74-82. doi: 10.13306/j.1672-3813.2016. 02.009.
[42]	VAZQUEZ A. Polynomial growth in branching processes with diverging reproductive number[J]. Physical Review Letters, 2006, 96(3): 038702. doi: 10.1103/PhysRevLett.96. 038702.
[43]	VAZQUEZ A, RACZ B, LUKACS A, et al. Impact of non- Poissonian activity patterns on spreading processes[J]. Physical Review Letters, 2007, 98(15): 158702. doi: 10.1103/ PhysRevLett.98.158702.
[44]	IRIBARREN J L. Impact of human activity patterns on the dynamics of information diffusion[J]. Physical Review Letters, 2009, 103(3): 038702. doi: 10.1103/PhysRevLett.103.038702.
[45]	MIN B, GOH K I, and VAZQUE A. Spreading dynamics following bursty human activity patterns[J]. Physical Review E, 2011, 83(3): 036102. doi: 10.1103/PhysRevE.83.036102.
[46]	Yang Z, Cui A X, and Zhou T. Impact of heterogeneous human activities on epidemic spreading[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(23): 4543-4548. doi: 10.1016/j.physa.2011.06.068.
[47]	吴联仁, 李瑾颉, 闫强. 基于时间异质性的微博信息传播模型[J]. 电子科技大学学报, 2015, 44(5): 657-662. doi: 10.3969/ j.issn.1001-0548.2015.05.003.
	WU L R, LI J J, and YAN Q. Micro-blog information spreading model based on temporal heterogeneity[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(5): 657-662. doi: 10.3969/j.issn.1001-0548.2015.05. 003.
[48]	KIM S. Self-organized pinning and interface growth in a random medium[J]. Physical Review Letters, 1992, 69(24): 3539. doi: 10.1103/PhysRevLett.69.3539.
[49]	STEHL J, BARRAT A, and BIANCONI G. Dynamical and bursty interactions in social networks[J]. Physical Review E, 2010, 81(3): 035101. doi: 10.1103/PhysRevE.81.035101.
[50]	JO H H, PAN R K, and KASKI K. Emergence of bursts and communities in evolving weighted networks[J]. PloS One, 2011, 6(8): e22687. doi: 10.1371/journal.pone.0022687.
[51]	YAN Q, WU L, LIU C, et al. Information propagation in online social network based on human dynamics[C]. Abstract and Applied Analysis, New York, USA, 2013: 1-7. doi: 10. 1155/2013/953406.
[52]	YAN Q, WU L, and YI L. Research on the human dynamics in mobile communities based on social identity[J]. Discrete Dynamics in Nature and Society, 2012, 2012. doi: 10.1155/ 2012/672756.
[53]	GRABOWICZ P A, RAMASCO J J, GONCALVES B, et al. Entangling mobility and interactions in social media[J]. PloS One, 2014, 9(3): e92196. doi: 10.1371/journal.pone.0092196.
[54]	GLEESON J P, WARD J A, O’SULLIVAN K P, et al. Competition-induced criticality in a model of meme popularity[J]. Physical Review Letters, 2014, 112(4): 048701. doi: 10.1103/PhysRevLett.112.048701.
[55]	ZHAO L, QIU X, WANG X, et al. Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks[J]. Physica A: Statistical Mechanics and Its Applications, 2013, 392(4): 987-994. doi: http: //dx.doi.org/10.1016/j.physa.2012.10.031.
[56]	HOLME P and SARAMAKI J. Temporal networks[J]. Physics Reports, 2012, 519(3): 97-125. doi: 10.1016/j.physrep. 2012.03.001.
[57]	LEE S, ROCHA L E C, LILJEROS F, et al. Exploiting temporal network structures of human interaction to effectively immunize populations[J]. PloS One, 2012, 7(5): e36439. doi: 10.1371/journal.pone.0036439.
[58]	BOCCALETTI S, BIANCONI G, CRIADO R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014, 544(1): 1-122. doi: 10.1016/j.physrep.2014. 07.001.
[59]	SALEHI M, SHARMA R, MARZOLLA M, et al. Spreading processes in multilayer networks[J]. IEEE Transactions on Network Science and Engineering, 2015, 2(2): 65-83. doi: 10.1109/TNSE.2015.2425961.
[60]	KIVELA M, ARENAS A, BARTHELEMY M, et al. Multilayer networks[J]. Journal of Complex Networks, 2014, 2(3): 203-271. doi: 10.1093/comnet/cnu016.