Methodology for disseminating information channels analysis in social networks

A. A. Pronoza   ; L. A. Vitkova   ; A. A. Chechulin   ; I. V. Kotenko   ; D. V. Sakharov

Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 14 (2018) no. 4, pp. 362-377

Résumé

The aim of the investigation was to develop a methodology for disseminating information channels analysis in social networks. The proposed methodology is based on the three steps. The first one is the formation of a knowledge base with information about the relationships between users and groups. The second one is the interactive mapping of information dissemination paths, and the third one is related to the visual analysis of the results obtained in previous steps. The interconnections of users and groups in the social network allow one to form the connectivity graphs, and the facts of information transfer through these channels make it possible to identify the ways of content distribution. The results of experiments that confirm the applicability of the proposed methodology are also presented in the paper.

Détail

Keywords: social network, visualization, protection against information, inappropriate information, connection graph, visual analytics.

Bibliographie

[1] Social networks in Russia, summer 2017: figures and trends (In Russian) (accessed: 20.08.2018)

[2] Kotenko I. V., Chechulin A. A., Shorov A. V., Komashinsky D. V., “Analysis and evaluation of web pages classification techniques for inappropriate content blocking”, 14th Industrial Conference on Data Mining, LNAI, 8557, Springer-Verlag, New York e.a., 2014, 39–54

[3] Novozhilov D. A., Kotenko I. V., Chechulin A. A., “Improving the categorization of web sites by analysis of Html-Tags Statistics to Block inappropriate content”, 9th Intern. Symposium on Intelligent Distributed Computing, Springer-Verlag, New York e.a., 2016, 257–263 | DOI

[4] Kotenko I. V., Chechulin A. A., Komashinsky D. V., “Categorisation of web pages for protection against inappropriate content in the Internet”, Intern. Journal of Internet Protocol Technology, 10:1 (2017), 61–71 | DOI

[5] Zadeh L., Abbasov A., Shahbazova S., “Fuzzy based techniques in human like processing of social network data”, Intern. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 23, Suppl. 1 (2015), 1–14 | DOI | MR

[6] Gomzin A., Kuznetsov S., “Methods of construction of socio-demographic profiles of Internet users”, Programming and Computer Software Journal, 27:4 (2015), 129–144

[7] Drobyshevskiy M., Korshunov A., Turdakov D., “Parallel modularity computation for directed weighted graphs with overlapping communities”, Programming and Computer Software Journal, 28:6 (2016), 153–170

[8] Barabasi A., Bonabeau E., “Scale-free networks”, Scientific American Journal, 288:5 (2003), 50–59 | MR

[9] Zhang E., Wang G., Gao K., Zhao X., Zhang Y., “Generalized structural holes finding algorithm by bisection in social communities”, 6th Intern. Conference on Genetic and Evolutionary Computing, 2013, 276–279

[10] Liu Q., Zhang L., “Information cascades in online reading: an empirical investigation of panel data”, Library Hi Tech Journal, 32:4 (2016), 687–705 | DOI

[11] Opsahl T., Agneessens F., Skvoretz J., “Node centrality in weighted networks: Generalizing degree and shortest paths”, Social Networks Journal, 32:3 (2010), 245–251 | DOI | MR

[12] Hickethier G., Tommelein I. D., Lostuvali B., “Social network analysis of information flow in an IPD-project design organization”, 21st Annual Conference of the Intern. Group for Lean Construction, 2013, 315–324

[13] Sudhahar S., Veltri G., Cristianini N., “Automated analysis of the US presidential elections using Big Data and network analysis”, Big Data $\$ Society Journal, 2:1 (2015), 1–28 | DOI

[14] Martinez A., Dimitriadis Y., Rubia B., Gomez E., de la Fuente P., “Combining qualitative evaluation and social network analysis for the study of classroom social interactions”, Computers $\$ Education Journal, 41:4 (2003), 353–368 | DOI

[15] Lazer D., Pentland A. S., Adamic L., Aral S., Barabasi A. L., “Life in the network: the coming age of computational social science”, Science Journal, 323:5915 (2009), 721 | DOI

[16] Melman S., Bobkov V., Cherkashin A., “Technology and system visualization of large amounts of synoptic data”, Journal Information Science and Control Systems, 3:45 (2015), 63–71

[17] Hornostal O., “System of cluster analysis and visualization of big data”, Intern. Scientific Journal Internauka, 1:6 (2016), 22–24

[18] Averbukh V., Manakov D., “Analysis and visualization of “big data””, Intern. Scientific Conference Parallel Computational Technologies (2015), 332–340

[19] Paraphrasing and synonymy of text — ParaPhraser.ru (In Russian) (accessed: 20.08.2018)

[20] Kolomeec M. V., Gonzalez-Granadillo G., Doynikova E. V., Chechulin A. A., Kotenko I. V., Debar H., “Choosing models for security metrics visualization”, Computer Network Security, Lecture Notes in Computer Science, 10446, Springer-Verlag, New York e. a., 2017, 75–87 | DOI

[21] Kolomeec M. V., Chechulin A. A., Pronoza A. A., Kotenko I. V., “Technique of data visualization example of network topology display for security monitoring”, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 7:1 (2016), 41–57 | MR