Contextual centrality: going beyond network structure
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s41598-020-62857-4.pdf
Description
Published version
Size
1.85 MB
Format
Adobe PDF
Checksum (MD5)
7cdd8ee25c85a4b6eb7c3d4577c575e8
Author(s)
Leng, Yan
Sella, Yehonatan
Ruiz, Rodrigo
Pentland, Alex
Date Issued
2020
Journal
Scientific Reports
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). Centrality is a fundamental network property that ranks nodes by their structural importance. However, the network structure alone may not predict successful diffusion in many applications, such as viral marketing and political campaigns. We propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, relevant node characteristics. It nicely generalizes and relates to standard centrality measures. We test the effectiveness of contextual centrality in predicting the eventual outcomes in the adoption of microfinance and weather insurance. Our empirical analysis shows that the contextual centrality of first-informed individuals has higher predictive power than that of other standard centrality measures. Further simulations show that when the diffusion occurs locally, contextual centrality can identify nodes whose local neighborhoods contribute positively. When the diffusion occurs globally, contextual centrality signals whether diffusion may generate negative consequences. Contextual centrality captures more complicated dynamics on networks than traditional centrality measures and has significant implications for network-based interventions.
MIT Department
MIT Connection Science (Research institute)
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41598-020-62857-4
