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Temporally Evolving Community Detection and Prediction in Content-Centric Networks

  • Ana Paula AppelEmail author
  • Renato L. F. Cunha
  • Charu C. Aggarwal
  • Marcela Megumi Terakado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

Abstract

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera (https://github.com/renatolfc/chimera-stf), a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach. Code related to this paper is available at: https://github.com/renatolfc/chimera-stf.

Notes

Acknowledgments

Charu C. Aggarwal’s research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ana Paula Appel
    • 1
    Email author
  • Renato L. F. Cunha
    • 1
  • Charu C. Aggarwal
    • 2
  • Marcela Megumi Terakado
    • 3
  1. 1.IBM ResearchSão PauloBrazil
  2. 2.IBM ResearchYorktownUSA
  3. 3.University of São PauloSão PauloBrazil

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