Ranking by inspiration: a network science approach

Abstract

Contagion processes have been widely studied in epidemiology and life science in general, but their implications are largely tangible in other research areas, such as in network science and computational social science. Contagion models, in particular, have proven helpful in the study of information diffusion, a very topical issue thanks to its applications to social media/network analysis, viral marketing campaigns, influence maximization and prediction. In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors, collaborators) to publish papers in the same topic, and the latter influence their neighbors in their turn. This well-accepted definition, however, does not consider that influence in bibliographic networks is a complex phenomenon involving several scientific and cultural aspects. In fact, in scientific citation networks, influential topics are usually considered those ones that spread most rapidly in the network. Although this is generally a fact, this semantics does not consider that topics in bibliographic networks evolve continuously. In fact, knowledge, information and ideas are dynamic entities that acquire different meanings when passing from one person to another. Thus, in this paper, we propose a new definition of influence that captures the diffusion of inspiration within the network. We call it inspiration score, and show its effectiveness in detecting the most inspiring topics, authors, papers and venues in a citation network built upon two large bibliographic datasets. We show that the inspiration score can be used as an alternative or complementary bibliographic index in academic ranking applications.

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Notes

  1. 1.

    https://www.natureindex.com/news-blog/italian-scientists-increase-self-citations-in-response-to-promotion-policy.

  2. 2.

    https://aminer.org/citation.

  3. 3.

    Dataset encoded in ArnetMiner V8 format, https://github.com/rupensa/tranet.

  4. 4.

    https://www.mongodb.com/.

  5. 5.

    https://github.com/bioglio/inspiration_score.

  6. 6.

    https://jupyter.org/.

  7. 7.

    https://www.google.com/forms/about/.

  8. 8.

    https://arxiv.org/.

  9. 9.

    The dataset is made available within the SNAP project at https://snap.stanford.edu/data/cit-HepTh.html.

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Acknowledgements

We wish to thank the anonymous reviewers for their valuable comments and suggestions. We also thank Prof. Igor Pesando for his support in interpreting the results on the high energy physics dataset. Finally, we thank all the researchers who participated in our survey for the assessment of part of the results. This work has been 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).

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Correspondence to Livio Bioglio.

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Editors: Takuya Kida, Takeaki Uno, Tetsuji Kuboyama, Akihiro Yamamoto.

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Bioglio, L., Rho, V. & Pensa, R.G. Ranking by inspiration: a network science approach. Mach Learn 109, 1205–1229 (2020). https://doi.org/10.1007/s10994-019-05828-9

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Keywords

  • Information diffusion
  • Bibliographic indexes
  • Citation networks
  • Topic modeling