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

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В.О. Чесноков
74 ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2017. № 2
Чесноков Владислав Олегович — аспирант, ассистент кафедры «Информационная
безопасность» МГТУ им. Н.Э. Баумана (Российская Федерация, 105005, Москва, 2-я Ба-
уманская ул., д. 5).
Просьба ссылаться на эту статью следующим образом:
Чесноков В.О. Предсказание атрибутов профиля пользователя социальной сети путем ана-
лиза сообществ графа его ближайшего окружения // Вестник МГТУ им. Н.Э. Баумана.
Сер. Приборостроение. 2017. № 2. C. 66–76. DOI: 10.18698/0236-3933-2017-2-66-76
PREDICTING ATTRIBUTES OF USER PROFILE IN SOCIAL NETWORKS
BY ANALYZING COMMUNITIES OF THEIR EGO-NETWORK
V.O. Chesnokov
Bauman Moscow State Technical University, Moscow, Russian Federation
Abstract Keywords
In online social networks, a user is allowed to specify a lot
of personal information — attributes. Some users provide
only a part of whole information, or do not provide any
information about themselves at all. Due to that, inferred
hidden attributes are one of the fundamental problems of
social analysis. The study proposes a new approach to
user's hidden or unspecified attributes prediction. The
method is based on analysis of the user's ego-network
structure and attributes of its social graph vertices. The
developed method was compared wth other methods
according to three datasets of users' ego-networks from
Facebook, Twitter and VKontakte social networks. It
showed high values of F-measure, precision and complete-
ness for predicting the chosen attributes of the user profile
such as hometown or school. Using this method with
additional data sources an analyst with high precision can
reveal the identity of an anonymous social network user by
their relations with other users
Social networks, social graph, com-
munity detection, profile prediction
REFERENCES
[1] Davis C.A.J., Pappa G.L., de Oliveira D.R.R., Arcanjo F. de Zima. Inferring the location of twit-
ter messages based on user relationships. T. GIS., 2011, vol. 15, no. 6, pp. 735–751.
DOI: 10.1111/j.1467-9671.2011.01297.x
Available at: /abstract
[2] Li R., Wang C., Chang K. C.-C. User profiling in an ego network: co-profiling attributes and
relationships. Proc. 23rd Int. Conf. on World Wide Web. WWW '14. New York, ACM, 2014,
pp. 819–830.
Available at: p819.pdf
[3] Dong Y., Yang Y., Tang J., Yang Y., Chawla N.V. Inferring user demographics and social strate-
gies in mobile social networks. Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and
Data Mining. KDD '14. New York, ACM, 2014, pp. 15–24.