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2018 | OriginalPaper | Buchkapitel

Marrying Community Discovery and Role Analysis in Social Media via Topic Modeling

verfasst von : Gianni Costa, Riccardo Ortale

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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Abstract

We explore the adoption of topic modeling to inform the seamless integration of community discovery and role analysis. For this purpose, we develop a new Bayesian probabilistic generative model of social media, according to which the observation of social links and textual contents is governed by novel and intuitive relationships among latent content topics, communities and roles. Variational inference under the devised model allows for exploratory, descriptive and predictive tasks, including the detection and interpretation of overlapping communities, roles and topics as well as the prediction of missing links. Extensive tests on real-world social media reveal a superior accuracy of the proposed model in comparison to state-of-the-art competitors, which substantiates the rationality of the motivating intuition. The experimental results are also insightfully inspected from a qualitative viewpoint.

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Vorheriges Kapitel Adding Missing Words to Regular Expressions

Nächstes Kapitel Text Generation Based on Generative Adversarial Nets with Latent Variables

Notice that, in the case of collaboration networks, the term message refers to the corresponding type of coauthored content, such as, e.g., project proposals, deliverables and publications. In particular, one data set in Sect. 6 is chosen from the scientific collaboration domain and, in such a context, message is a synonym of publication.

The derivation of both the functional forms of the factors on the right hand side of Eq. 3 and the updates of the respective variational parameters is omitted for brevity.

Blei, D., Kucukelbir, A., McAuliffe, J.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017)MathSciNetCrossRef

Blei, D.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRef

Costa, G., Ortale, R.: A Bayesian hierarchical approach for exploratory analysis of communities and roles in social networks. In: Proceedings of IEEE/ACM ASONAM, pp. 194–201 (2012)

Costa, G., Ortale, R.: A unified generative Bayesian model for community discovery and role assignment based upon latent interaction factors. In: Proceedings of IEEE/ACM ASONAM, pp. 93–100 (2014)

Costa, G., Ortale, R.: Model-based collaborative personalized recommendation on signed social rating networks. ACM Trans. Internet Technol. 16(3), 20:1–20:21 (2016)CrossRef

Costa, G., Ortale, R.: Mining overlapping communities and inner role assignments through Bayesian mixed-membership models of networks with context-dependent interactions. ACM Trans. Knowl. Discov. Data 12(2), 18:1–18:32 (2018)CrossRef

Gopalan, P., Hofman, J., Blei, D.: Scalable recommendation with hierarchical poisson factorization. In: Proceedings of UAI, pp. 326–335 (2015)

Henderson, K., Rad, T.E.: Applying latent dirichlet allocation to group discovery in large graphs. In: Proceedings of ACM SAC, pp. 1456–1461 (2009)

McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on enron and academic email. J. Artif. Intell. Res. 30(1), 249–272 (2007)

10.

Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of ACM WSDM, pp. 587–596 (2013)

11.

Zhang, H., Qiu, B., Giles, C., Foley, H., Yen, J.: An LDA-based community structure discovery approach for large-scale social networks. In: IEEE Intelligence and Security Informatics, pp. 200–207 (2007)

Titel: Marrying Community Discovery and Role Analysis in Social Media via Topic Modeling
verfasst von: Gianni Costa
Riccardo Ortale
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93036-7

Electronic ISBN: 978-3-319-93037-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93037-4_7

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