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2017 | OriginalPaper | Chapter

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

Authors : Livio Bioglio, Ruggero G. Pensa, Valentina Rho

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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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.

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Footnotes
Literature
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Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
2.
Gui, H., Sun, Y., Han, J., Brova, G.: Modeling topic diffusion in multi-relational bibliographic information networks. In: Proceedings of CIKM 2014, pp. 649–658. ACM (2014)
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Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010)
4.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD 2008, pp. 990–998 (2008)
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Xiong, C., Power, R., Callan, J.: Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of WWW 2017, pp. 1271–1279. ACM (2017)
Metadata
Title
TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks
Authors
Livio Bioglio
Ruggero G. Pensa
Valentina Rho
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71272-7

Electronic ISBN: 978-3-319-71273-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71273-4_46

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