Предсказание атрибутов профиля пользователя социальной сети путем анализа сообществ графа его ближайшего окружения

10 / 11

Предсказание атрибутов профиля пользователя социальной сети…
ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2017. № 2 75
[4] Korshunov A., Beloborodov I., Buzun N., Avanesov V., et al. Social network analysis: methods
and applications. Trudy Instituta sistemnogo programmirovaniya RAN [Proceedings of ISP RAS],
2014, vol. 26, no. 1, pp. 439–456 (in Russ.).
Available at:
[5] Mislove A., Viswanath B., Gummadi K.P., Druschel P. You are who you know: inferring user
profiles in online social networks. Proc. 3d ACM Int. Conf. on Web Search and Data Mining.
WSDM '10. New York, ACM, 2010, pp. 251–260.
[6] Chaabane A., Acs G., Kaafar M. You are what you like! Information leakage through users’ in-
terests. Proc. Annual Network and Distributed System Security Symposium, 2012.
[7] Kosinski M., Stillwell D., Graepel T. Private traits and attributes are predictable from digital
records of human behavior. Proc. of the National Academy of Sciences, 2013, vol. 110, no. 15,
pp. 5802–5805. DOI: 10.1073/pnas.1218772110
Available at:
[8] Dougnon R.Y., Fournier-Viger P., Nkambou R. Advances in artificial intelligence. Proc. 28th
Canadian Conf. on Artificial Intelligence. Canada, Springer International Publishing, 2015,
pp. 84–99.
[9] Yang J., Leskovec J. Community-affiliation graph model for overlapping network community
detection. 12th IEEE Int. Conf. on Data Mining, ICDM 2012. 2012, pp. 1170–1175.
DOI: 10.1109/ICDM.2012.139 Available at:
[10] Chesnokov V.O. Intersecting communities allocation in social graphs based on majoritarian
neighbours characteristic. LOMONOSOV–2016. XXIII Mezhd. nauch. konf. studentov, aspirantov i
molodykh uchenykh [LOMONOSOV-2016. XXIII Int. conf. of students, postgraduates and young
scientists]. Moscow, MAKS Press Publ., 2016, pp. 49–51.
[11] Clauset A., Newman M.E.J., Moore C. Finding community structure in very large networks.
Phys. Rev. E., 2004, vol. 70, no. 6, pp. 1–6. DOI: 10.1103/PhysRevE.70.066111
Available at:
[12] Rosvall M., Bergstrom C.T. Maps of random walks on complex networks reveal community
structure. Proc. of the National Academy of Sciences, 2008, vol. 105, no. 4, pp. 1118–1123.
DOI: 10.1073/pnas.0706851105 Available at:
[13] Yang J., Leskovec J. Overlapping community detection at scale: a nonnegative matrix factoriza-
tion approach. Proc. of the 6th ACM Int. Conf. on Web Search and Data Mining. WSDM '13. New
York, 2013, pp. 587–596. DOI: 10.1145/2433396.2433471
Available at:
[14] Yang J., McAuley J.J., Leskovec J. Community detection in networks with node attributes. 2013
IEEE 13th Int. Conf. on Data Mining. 2013, pp. 1151–1156.
[15] Leskovec J., Krevl A. SNAP datasets: stanford large network dataset collection. Stanford Net-
work Analysis Project: website. Available at: (accessed 12.01.2017).
[16] Chesnokov V.O., Klyucharev P.G. Social graph community differentiated by node features
with partly missing information. Nauka i obrazovanie: nauchnoe izdanie MGTU im. N.E. Baumana
[Science and Education: Scientific Publication of BMSTU], 2015, no. 9, pp. 188–199 (in Russ.).
DOI: 10.7463/0915.0811704
Chesnokov V.O. — assistant, post-graduate student of Information Security Department,
Bauman Moscow State Technical University (2-ya Baumanskaya ul. 5, Moscow, 105005
Russian Federation).