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Exploring influence and interests among users within social networks. / Simoes, Jose; Kiseleva, Julia; Sivogolovko, Elena; Novikov, Boris.

Computational Social Networks: Security and Privacy. Vol. 9781447140511 Springer Nature, 2012. p. 177-206.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

Harvard

Simoes, J, Kiseleva, J, Sivogolovko, E & Novikov, B 2012, Exploring influence and interests among users within social networks. in Computational Social Networks: Security and Privacy. vol. 9781447140511, Springer Nature, pp. 177-206. https://doi.org/10.1007/978-1-4471-4051-1_8

APA

Simoes, J., Kiseleva, J., Sivogolovko, E., & Novikov, B. (2012). Exploring influence and interests among users within social networks. In Computational Social Networks: Security and Privacy (Vol. 9781447140511, pp. 177-206). Springer Nature. https://doi.org/10.1007/978-1-4471-4051-1_8

Vancouver

Simoes J, Kiseleva J, Sivogolovko E, Novikov B. Exploring influence and interests among users within social networks. In Computational Social Networks: Security and Privacy. Vol. 9781447140511. Springer Nature. 2012. p. 177-206 https://doi.org/10.1007/978-1-4471-4051-1_8

Author

Simoes, Jose ; Kiseleva, Julia ; Sivogolovko, Elena ; Novikov, Boris. / Exploring influence and interests among users within social networks. Computational Social Networks: Security and Privacy. Vol. 9781447140511 Springer Nature, 2012. pp. 177-206

BibTeX

@inbook{c3531c7799e74af3b033c8f7655d6e5b,
title = "Exploring influence and interests among users within social networks",
abstract = "The spread of influence among individuals in a social network is one of the fundamental questions in the social sciences. In this chapter we consider the main definitions of influence, which are based on a small set of snapshot observations of a social network. The former is particularly useful because large-scale social network data sets are often only available in snapshots or crawls. In our work, considering a rich dataset of user preferences and interactions, we use clustering techniques to study how user interests group together and identify the most popular users within these groups. For this purpose, we focus on multiple dimensions of users-related data, providing a more detailed process model of how influence spreads. In parallel, we study the measurement of influence within the network according to interest dependencies. We validate our analysis using the history of user social interactions on Facebook. Furthermore, this chapter shows how these ideas can be applied in real-world scenarios, namely for recommendation and advertising systems.",
author = "Jose Simoes and Julia Kiseleva and Elena Sivogolovko and Boris Novikov",
year = "2012",
month = aug,
day = "1",
doi = "10.1007/978-1-4471-4051-1_8",
language = "English",
isbn = "1447140508",
volume = "9781447140511",
pages = "177--206",
booktitle = "Computational Social Networks",
publisher = "Springer Nature",
address = "Germany",

}

RIS

TY - CHAP

T1 - Exploring influence and interests among users within social networks

AU - Simoes, Jose

AU - Kiseleva, Julia

AU - Sivogolovko, Elena

AU - Novikov, Boris

PY - 2012/8/1

Y1 - 2012/8/1

N2 - The spread of influence among individuals in a social network is one of the fundamental questions in the social sciences. In this chapter we consider the main definitions of influence, which are based on a small set of snapshot observations of a social network. The former is particularly useful because large-scale social network data sets are often only available in snapshots or crawls. In our work, considering a rich dataset of user preferences and interactions, we use clustering techniques to study how user interests group together and identify the most popular users within these groups. For this purpose, we focus on multiple dimensions of users-related data, providing a more detailed process model of how influence spreads. In parallel, we study the measurement of influence within the network according to interest dependencies. We validate our analysis using the history of user social interactions on Facebook. Furthermore, this chapter shows how these ideas can be applied in real-world scenarios, namely for recommendation and advertising systems.

AB - The spread of influence among individuals in a social network is one of the fundamental questions in the social sciences. In this chapter we consider the main definitions of influence, which are based on a small set of snapshot observations of a social network. The former is particularly useful because large-scale social network data sets are often only available in snapshots or crawls. In our work, considering a rich dataset of user preferences and interactions, we use clustering techniques to study how user interests group together and identify the most popular users within these groups. For this purpose, we focus on multiple dimensions of users-related data, providing a more detailed process model of how influence spreads. In parallel, we study the measurement of influence within the network according to interest dependencies. We validate our analysis using the history of user social interactions on Facebook. Furthermore, this chapter shows how these ideas can be applied in real-world scenarios, namely for recommendation and advertising systems.

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U2 - 10.1007/978-1-4471-4051-1_8

DO - 10.1007/978-1-4471-4051-1_8

M3 - Chapter

AN - SCOPUS:84930352214

SN - 1447140508

SN - 9781447140474

VL - 9781447140511

SP - 177

EP - 206

BT - Computational Social Networks

PB - Springer Nature

ER -

ID: 36627495