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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Evaluating link prediction by diffusion processes in dynamic networks

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Author(s):
Vega-Oliveros, Didier A. [1, 2] ; Zhao, Liang [1] ; Berton, Lilian [3]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Dept Comp & Math, Ribeirao Preto, SP - Brazil
[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 - USA
[3] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 9, JUL 25 2019.
Web of Science Citations: 0
Abstract

Link prediction (LP) permits to infer missing or future connections in a network. The network organization defines how information spreads through the nodes. In turn, the spreading may induce changes in the connections and speed up the network evolution. Although many LP methods have been reported in the literature, as well some methodologies to evaluate them as a classification task or ranking problem, none have systematically investigated the effects on spreading and the structural network evolution. Here, we systematic analyze LP algorithms in a framework concerning: (1) different diffusion process - Epidemics, Information, and Rumor models; (2) which LP method most improve the spreading on the network by the addition of new links; (3) the structural properties of the LP-evolved networks. From extensive numerical simulations with representative existing LP methods on different datasets, we show that spreading improve in evolved scale-free networks with lower shortest-path and structural holes. We also find that properties like triangles, modularity, assortativity, or coreness may not increase the propagation. This work contributes as an overview of LP methods and network evolution and can be used as a practical guide of LP methods selection and evaluation in terms of computational cost, spreading capacity and network structure. (AU)

FAPESP's process: 18/01722-3 - Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications
Grantee:Lilian Berton
Support type: Regular Research Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 18/24260-5 - Spatiotemporal data analytics based on complex networks
Grantee:Didier Augusto Vega Oliveros
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants
FAPESP's process: 16/23698-1 - Dynamical processes in complex network based on machine learning
Grantee:Didier Augusto Vega Oliveros
Support type: Scholarships in Brazil - Post-Doctorate