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International Journal of Data Science and Analytics
Erschienen in: International Journal of Data Science and Analytics 4/2020

05.06.2019 | Regular Paper

Topic-aware joint analysis of overlapping communities and roles in social media

verfasst von: Gianni Costa, Riccardo Ortale

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 4/2020

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Abstract

Topic modeling can be used to improve the mutuality and interpenetration of community discovery and role analysis in social media. Also, it is useful to uncover communities and roles that are both social and topic-aware. In the present manuscript, we explore the exploitation of topic modeling to inform the seamless integration of community discovery and role analysis. For this purpose, we develop an innovative generative model of social media, in which the interrelation among communities, roles and topics is explained from a fully Bayesian perspective. Essentially, communities, roles and topics are latent factors that interact in an underlying generative process, to govern link formation and message wording. Posterior inference under the devised model allows for a variety of exploratory, descriptive and predictive tasks. These include the detection and interpretation of overlapping communities, roles and topics as well as the prediction of missing links. We derive the mathematical details of variational inference and design a coordinate-ascent algorithm implementing the latter. An empirical assessment on real-world social media demonstrates a superior accuracy of the proposed model in community discovery and link prediction compared to several established competitors, which substantiates the rationality of both our modeling effort and the underlying intuition.
Vorheriger Artikel ICANE: interaction content-aware network embedding via co-embedding of nodes and edges
Nächster Artikel GINN: gradient interpretable neural networks for visualizing financial texts

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Fußnoten
1
Notice that, in the case of collaboration networks, the term message refers to the corresponding type of coauthored content, such as project proposals, deliverables and publications. In particular, one data set used for the experimental assessment of Sect. 6 is chosen from the scientific collaboration domain and, in such a context, message is a synonym of publication.
 
2
The mathematical derivation both of the functional forms of the individual factors on the right hand side of Eq. 3 and the updates of the respective variational parameters is omitted for brevity.
 
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Metadaten
Titel
Topic-aware joint analysis of overlapping communities and roles in social media
verfasst von
Gianni Costa
Riccardo Ortale
Publikationsdatum
05.06.2019
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 4/2020
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-019-00190-4

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