This paper presents a system for tracking and analyzing the evolution and transformation of topics in an information network. The system consists of four main modules for pre-processing, adaptive topic modeling, network creation and temporal network analysis. The core module is built upon an adaptive topic modeling algorithm adopting a sliding time window technique that enables the discovery of groundbreaking ideas as those topics that evolve rapidly in the network.

TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks

Bioglio, Livio
Co-first
;
Pensa, Ruggero G.
Last
;
Rho, Valentina
Co-first
2017-01-01

Abstract

This paper presents a system for tracking and analyzing the evolution and transformation of topics in an information network. The system consists of four main modules for pre-processing, adaptive topic modeling, network creation and temporal network analysis. The core module is built upon an adaptive topic modeling algorithm adopting a sliding time window technique that enables the discovery of groundbreaking ideas as those topics that evolve rapidly in the network.
2017
2017 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2017)
Skopje, Macedonia
18-22 September 2017
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III
Springer International Publishing AG
10536
432
436
978-3-319-71272-7
978-3-319-71273-4
https://link.springer.com/chapter/10.1007/978-3-319-71273-4_46
Information diffusion, Topic modeling, Citation networks
Bioglio, Livio; Pensa, Ruggero G.; Rho, Valentina
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1655321
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