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

  • Livio Bioglio
  • Ruggero G. PensaEmail author
  • Valentina Rho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)


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.


Information diffusion Topic modeling Citation networks 



This work is partially funded by project MIMOSA (MultIModal Ontology-driven query system for the heterogeneous data of a SmArtcity, “Progetto di Ateneo Torino_call2014_L2_157”, 2015–17).


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 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)Google Scholar
  5. 5.
    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)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TurinTurinItaly

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