Paper Info
Title
Measuring The Inspiration Rate Of Topics In Bibliographic Networks
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.
Year
DOI
Venue
2017
10.1007/978-3-319-67786-6_22
DISCOVERY SCIENCE, DS 2017
Keywords
Field
DocType
Information diffusion, Topic modeling, Citation networks
Data science,Publication,Data mining,Scientific citation,Viral marketing,Social media,Computer science,Network analysis,Phenomenon,Topic model,Semantics
Conference
Volume
ISSN
Citations 
10558
0302-9743
1
PageRank 
References 
Authors
0.36
16
3
Name
Order
Citations
PageRank
Livio Bioglio1277.56
Valentina Rho2354.42
Ruggero G. Pensa335431.20