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Benchmarking measures of network influence

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Abstract
Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.
Keywords
COMPLEX NETWORKS, GRAPHS, NODES, SPREADERS, MODELS, DIFFUSION

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Please use this url to cite or link to this publication:

MLA
Bramson, Aaron, and Benjamin Vandermarliere. “Benchmarking Measures of Network Influence.” SCIENTIFIC REPORTS 6 (2016): n. pag. Print.
APA
Bramson, A., & Vandermarliere, B. (2016). Benchmarking measures of network influence. SCIENTIFIC REPORTS, 6.
Chicago author-date
Bramson, Aaron, and Benjamin Vandermarliere. 2016. “Benchmarking Measures of Network Influence.” Scientific Reports 6.
Chicago author-date (all authors)
Bramson, Aaron, and Benjamin Vandermarliere. 2016. “Benchmarking Measures of Network Influence.” Scientific Reports 6.
Vancouver
1.
Bramson A, Vandermarliere B. Benchmarking measures of network influence. SCIENTIFIC REPORTS. 2016;6.
IEEE
[1]
A. Bramson and B. Vandermarliere, “Benchmarking measures of network influence,” SCIENTIFIC REPORTS, vol. 6, 2016.
@article{8163065,
  abstract     = {{Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.}},
  articleno    = {{34052}},
  author       = {{Bramson, Aaron and Vandermarliere, Benjamin}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  keywords     = {{COMPLEX NETWORKS,GRAPHS,NODES,SPREADERS,MODELS,DIFFUSION}},
  language     = {{eng}},
  pages        = {{8}},
  title        = {{Benchmarking measures of network influence}},
  url          = {{http://dx.doi.org/10.1038/srep34052}},
  volume       = {{6}},
  year         = {{2016}},
}

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