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Sequential stacking link prediction algorithms for temporal networks.
He, Xie; Ghasemian, Amir; Lee, Eun; Clauset, Aaron; Mucha, Peter J.
Afiliación
  • He X; Department of Mathematics, Dartmouth College, Hanover, NH, USA.
  • Ghasemian A; Yale Institute for Network Science, Yale University, New Haven, CT, USA.
  • Lee E; Department of Scientific Computing, Pukyong National University, Busan, South Korea.
  • Clauset A; Department of Computer Science, University of Colorado, Boulder, CO, USA.
  • Mucha PJ; BioFrontiers Institute, University of Colorado, Boulder, Boulder, CO, USA.
Nat Commun ; 15(1): 1364, 2024 Feb 14.
Article en En | MEDLINE | ID: mdl-38355612
ABSTRACT
Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and on two temporal stochastic block models achieves near-oracle-level performance when combined with other single predictor methods as an ensemble learning method. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido