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Estimation method of the cohesion degree for the users’ profiles of social network based on open data

https://doi.org/10.21686/1818-4243-2017-6-14-22

Abstract

The purpose of research was to study the existing methods of determining the degree of cohesion of two users of social network, identifying their shortcomings and developing a new method. The research identified shortcomings of existing methods and proposed a new method for assessing the degree of cohesion of social network profiles based on open data from a social network. Under the degree of cohesion of users’ profiles is understood the probability of communication (interaction) of profile owners in real life, it is calculated for two users of the social network and expressed in percent. The work of the method is demonstrated on the example of the social network “In contact”. This method includes the sequence of the following stages: the first stage is data collection about users of the social network with API and the formation of tuples of users’ profile characteristics. A tuple of characteristics of social network profiles is the data, collected for each user, stored in a structured form.

The next step is the analysis of the collected information. For each characteristic of the tuple of profiles, i.e. the possible element of interaction of users in the social network, the coefficient of cohesion by the characteristic is calculated. In addition, for each feature, its informativeness is calculated, i.e. how important is this or that feature in this social network. At the final stage, the results are generated, using the formula for the probability of communication between two users, derived during the investigation. Obtained as a result of the application of the method, the probability of communication between two users can be used to optimize the activities of the operative-search services and special bodies.

In addition, the received degree of cohesion of two users can be interpreted as the probability of a channel of information leakage between them. The role of the user of the method can be any private or state organization that cares about the security of corporate data and commercial secrets, the operative-search service, as well as an organization that investigates cybercrimes and information security incidents. 

About the Authors

Valentina A. Kataeva
ITMO University, Saint Petersburg
Russian Federation
Master student


Igor S. Pantyukhin
ITMO University, Saint Petersburg
Russian Federation
Assistant


Igor V. Yurin
ITMO University, Saint Petersburg
Russian Federation
Cand. Sci. (Military), Assistant professor


References

1. Alekseev V.E., Teoriya grafov. Elektronnoe uchebno-metodicheskoe posobie. Nizhniy Novgorod: NNGU im. Lobachevskogo, 2012. 60 p. (In Russ.)

2. Bessonova E.E., Metod identifikatsii pol’zovateley v seti Internet s ispol’zovaniem komponentnogo profilya. Material dissertatsionnoy raboty na soiskanie uchenoy stepeni kandidata tekhnicheskikh nauk. Sankt-Peterburg, 2014. 115 p. (In Russ.)

3. Golovanova I.S., Vybor informativnykh priznakov. Otsenka informativnosti. Metodicheskie ukazaniya k laboratornoy rabote po distsipline «Metody obrabotki biomeditsinskikh dannykh». Tomsk, TPU. 2003. 18 p. (In Russ.)

4. Tarasov V.N., Eksponentsial’nyy zakon raspredeleniya. Matematicheskaya teoriya nadezhnosti. Uchebno-metodicheskiy kompleks po distsipline «Matematicheskaya teoriya nadezhnosti». Samara, PGUTI. 2012. 204 p. (In Russ.)

5. Kanaly utechki informatsii. Vikipediya: svobodnaya entsiklopediya / URL: https://ru.wikipedia. org/wiki/Kanaly_utechki_informatsii (accessed: 11.04.2016) (In Russ.)

6. Proverka statisticheskikh gipotez. Professional’nyy informatsionno-analiticheskiy resurs, posvyashchennyy mashinnomu obucheniyu, raspoznavaniyu obrazov i intellektual’nomu analiz dannykh / URL: http://www.machinelearning.ru/ wiki/index.php?title=Proverka_statisticheskikh_ gipotez# (accessed: 20.04.2016) (In Russ.)

7. Sotsial’naya set’. Wikipedia: svobodnaya entsiklopediya / URL: https://ru.wikipedia.org/wiki/ Sotsial’naya_set’ (accessed: 17.03.2016) (In Russ.).

8. Utechka korporativnykh dannykh cherez sotsial’nye seti. Press-tsentr kompanii Serchinform / URL: http://searchinform.ru/press/articles/777/ (accessed 20.04.2016) (In Russ.)

9. Bazovaya model’ ugroz bezopasnosti personal’nykh dannykh pri ikh obrabotke v informatsionnykh sistemakh personal’nykh dannykh (vypiska). Federal’naya sluzhba po tekhnicheskomu i eksportnomu kontrolyu. Moscow: 2008. 69 p. (In Russ.)

10. Mislove A., Measurement and Analysis of Online Social Networks / Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi. Proceedings of the 5th ACM/USENIX Internet Measurement Conference. 2007. P. 29–42.

11. William H. Hsu, Structural Link Analysis from User Profiles and Friends Networks: A Feature Construction Approach / William H. Hsu, Joseph Lancaster, Martin S.R. Paradesi, Tim Weninger. International Conference on Weblogs and Social Media. 2007.

12. Pantyukhin I.S., Zikratov I.A. Metodika provedeniya postintsidentnogo vnutrennego audita sredstv vychislitel’noy tekhniki. Nauchnotekhnicheskiy vestnik informatsionnykh tekhnologiy, mekhaniki i optiki. 2017. T. 17. No. 3. P. 467–474. doi: 10.17586/2226-1494-2017-17-3-467-474 (In Russ.)

13. Pantyukhin I.S., Zikratov I.A., Levina A.B. Metod provedeniya postintsidentnogo vnutrennego audita sredstv vychislitel’noy tekhniki na osnove grafov. Nauchno-tekhnicheskiy vestnik informatsionnykh tekhnologiy, mekhaniki i optiki. 2016. Vol. 16. No. 3. P. 506–512. doi: 10.17586/2226- 1494-2016-16-3-506-512 (In Russ.)

14. Yurasov D.S., Zikratov I.A. Razlichenie pol’zovateley na osnove ikh povedeniya v seti Internet. Nauchno-tekhnicheskiy vestnik informatsionnykh tekhnologiy, mekhaniki i optiki. 2013. No. 6(88). P. 148-151 (In Russ.)

15. Bessonova E.E., Zikratov I.A., Roskov V.Yu. Analiz sposobov identifikatsii pol’zovateley v seti Internet. Nauchno-tekhnicheskiy vestnik informatsionnykh tekhnologiy, mekhaniki i optiki. 2012. No. 6(82). P. 128–130 (In Russ.).

16. Vorob’eva A.A. Metodika identifikatsii internet-pol’zovatelya na osnove stilisticheskikh i lingvisticheskikh kharakteristik korotkikh elektronnykh soobshcheniy. Informatsiya i kosmos. 2017. No. 1. P. 127–130 (In Russ.).

17. Vorob’eva A.A. Analiz vozmozhnosti primeneniya razlichnykh lingvisticheskikh kharakteristik dlya identifikatsii avtora anonimnykh korotkikh soobshcheniy v global’noy seti Internet. Informatsiya i kosmos. 2014. No. 1. P. 42–46 (In Russ.).

18. V.V. Bykova, A.V. Kataeva Metody i sredstva analiza informativnosti priznakov pri obrabotke meditsinskikh dannykh. Programmnye produkty i sistemy. 2016. No. 2 (114). P. 172–178 (In Russ.).

19. Shannon, C.E. The Mathematical Theory of Communication / C.E.Shannon and W.Weaver. Urbana, IL: University of Illinois Press. ISBN: 0252725484. 1963. 144 p.

20. Glazkova A.V., Matematicheskoe modelirovanie klassifikatsii ob»ektov (na primere opredeleniya kategorii ponentsial’nykh adresatov teksta). Material dissertatsionnoy raboty na soiskanie uchenoy stepeni kandidata tekhnicheskikh nauk. Tyumen’. 2016. 141 p. (In Russ.)


Review

For citations:


Kataeva V.A., Pantyukhin I.S., Yurin I.V. Estimation method of the cohesion degree for the users’ profiles of social network based on open data. Open Education. 2017;(6):14-22. (In Russ.) https://doi.org/10.21686/1818-4243-2017-6-14-22

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ISSN 1818-4243 (Print)
ISSN 2079-5939 (Online)