[Purpose/significance]In order to deal with the quick birth-death process and unconspicuous hotspot characteristics of information transmission nodes, a new detecting and prognosis system is proposed based on complex networks. [Method/process]In order to deal with those problems, a novel micro-blog information transmission hotspot inward node prognosis algorithm was proposed as the IPIN algorithm based on complex network analysis methods. This paper used this algorithm to build a model with complex network node relations, and found relation sub-networks by related nodes. Then, the thermal power spectrum computing was used to dope out information transmission ranges and prospective effects. [Result/conclusion]Data experiment results prove that the IPIN algorithm has higher hot transmission node coverage rate, accuracy rate and better cost-performance than those of the SNMA algorithm.
Wang Linsen
,
Wang Xueyi
. Research on the Detecting and Prognosis Algorithm of the Micro-blog Hotspot Transmission Inward Node[J]. Library and Information Service, 2018
, 62(3)
: 71
-77
.
DOI: 10.13266/j.issn.0252-3116.2018.03.009
[1] 吴小兰,章成志.基于突发事件特征网络的用户社区发现与社区主题演化研究——以新浪微博H7N9 事件为例[J].情报理论与实践,2017,40(5):94-99.
[2] 刘健,毕强,李瑞.微博舆情信息传播效果评价指标体系构建研究——基于模糊数据包络分析法[J].情报理论与实践,2016,39(12):31-38.
[3] APARICIO S, VILLAZÓN-TERRAZAS J, ÁLVAREZ G. A model for scale-free networks: application to Twitter[J]. Journal of entropy, 2015,17(10):5848-5867.
[4] COLETTO M, LUCCHESE C, ORLANDO S. Twitter for election forecasts: a joint machine learning and complex network approach applied to an Italian case study [J]. Journal of computer networks, 2016, 32(8):234-238.
[5] MAHARANI W, GOZALI A A. Collaborative social network analysis and content-based approach to improve the marketing strategy of SMEs in Indonesia [C]//International conference on computer science and computational intelligence. New York: Curran Associates Inc, 2015:373-381.
[6] CHELMIS C. Complex modeling and analysis of workplace collaboration data[J]. Journal of communication, 2017,14(3):503-508.
[7] GAO Q, ABEL F, HOUBEN G. A comparative study of users’ microblogging behavior on Sina Weibo and Twitter [J]. Journal of computer, 2016,31(3):632-644.
[8] DONG G, YANG W. Detecting community pacemakers of burst topic in Twitter [J]. Journal of information systems, 2016,3(9):245-255.
[9] MAIRA G, PAULA A A, CLAUDIO P. Large-scale multi-agent-based modeling and simulation of microblogging-based online social network [J]. Journal of communication, 2017,11(3):627-638.
[10] SHUAI X, DIN Y, BUSEMEYER J. Modeling indirect influence on Twitter [J]. Journal of social computing, 2016,8(5):1-17.
[11] LAM A J. Improving Twitter community detection through contextual sentiment analysis of Tweets [C]//Proceedings of the 54th annual meeting of the Association for Computational Linguistics. Stroudsburg: Valencia Press, 2016:30-36.
[12] LIAO W. Strategies for spreading information from local to global in social complex networks, cases from a village in China [J]. Journal of computer networks, 2017,33(2):12-28.
[13] FORNACCIARI P, MORDONINI M, TOMAUIOLO M. Social network and sentiment analysis on Twitter: towards a combined approach [J]. Journal of multi-media computing, 2016, 3(1):787-793.
[14] ALZAHRANI S, ALASHRI S, KOPPELA A R. A network-based model for predicting hashtag breakouts in Twitter [J]. Journal of social computing, 2016,8(11):423-453.
[15] SIBBALD P. Using social media as a pedagogical tool in graduate public health education and training [J]. Journal of healthcare communications, 2016,1(2):4-12.
[16] ZHAO J, WANG L. Research on public opinion propagation in micro-blogging based on epidemic models [J]. Journal of information and management, 2016,13(2):235-244.
[17] JIN X, WANG Y. Research on social network structure and public opinions dissemination of micro-blog based on complex network analysis [J]. Journal of networks, 2015,8(7):1543-1551.
