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Connecting network science and information theory

Author

Listed:
  • de Arruda, Henrique F.
  • Silva, Filipi N.
  • Comin, Cesar H.
  • Amancio, Diego R.
  • Costa, Luciano da F.

Abstract

A framework integrating information theory and network science is proposed. By incorporating and integrating concepts such as complexity, coding, topological projections and network dynamics, the proposed network-based framework paves the way not only to extending traditional information science, but also to modeling, characterizing and analyzing a broad class of real-world problems, from language communication to DNA coding. Basically, an original network is supposed to be transmitted, with or without compaction, through a sequence of symbols or time-series obtained by sampling its topology by some network dynamics, such as random walks. We show that the degree of compression is ultimately related to the ability to predict the frequency of symbols based on the topology of the original network and the adopted dynamics. The potential of the proposed approach is illustrated with respect to the efficiency of transmitting several types of topologies by using a variety of random walks. Several interesting results are obtained, including the behavior of the Barabási–Albert model oscillating between high and low performance depending on the considered dynamics, and the distinct performances obtained for two geographical models.

Suggested Citation

  • de Arruda, Henrique F. & Silva, Filipi N. & Comin, Cesar H. & Amancio, Diego R. & Costa, Luciano da F., 2019. "Connecting network science and information theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 641-648.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:641-648
    DOI: 10.1016/j.physa.2018.10.005
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    References listed on IDEAS

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    1. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    2. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    3. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
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    Cited by:

    1. Guerreiro, Lucas & Silva, Filipi N. & Amancio, Diego R., 2024. "Recovering network topology and dynamics from sequences: A machine learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    2. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).

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