Abstract
Information diffusion is a widely-studied topic 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 propose a measure of the inspiration rate called inspiration rank. Finally, we show the effectiveness of our measure in detecting the most inspiring topics in a citation network built upon a large bibliographic dataset.
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Notes
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Dataset encoded in ArnetMiner V8 format, https://github.com/rupensa/tranet.
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Acknowledgments
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).
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Bioglio, L., Rho, V., Pensa, R.G. (2017). Measuring the Inspiration Rate of Topics in Bibliographic Networks. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_22
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