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.

Measuring the Inspiration Rate of Topics in Bibliographic Networks

BIOGLIO, LIVIO
First
;
RHO, VALENTINA;PENSA, Ruggero Gaetano
Last
2017-01-01

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.
2017
The 20th International Conference on Discovery Science (DS 2017)
Kyoto, Japan
October 15-17, 2017
Discovery Science, Proceedings of the 20th International Conference, DS 2017, Kyoto, Japan, October 15–17, 2017
Springer
10558
309
323
978-3-319-67785-9
978-3-319-67786-6
https://link.springer.com/chapter/10.1007/978-3-319-67786-6_22
information diffusion, topic modeling, citation networks
Bioglio, Livio; Rho, Valentina; Pensa, Ruggero G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1648155
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