| Full text | |
| Author(s): |
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: | 18/24260-5 - Spatiotemporal Data Analytics based on Complex Networks |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| 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 Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 15/50122-0 - Dynamic phenomena in complex networks: basics and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |