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Award ID contains: 2220292

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  1. ; ; (, IEEE Transactions on Signal Processing)
    Free, publicly-accessible full text available December 1, 2025
  2. ; ; ; (, IEEE)
    Free, publicly-accessible full text available September 22, 2025
  3. ; ; ; ; ; ; ; ; ; ; et al (, ACM)
    Free, publicly-accessible full text available August 4, 2025
  4. ; ; ; ; ; ; ; ; ; (, ACM)
    Free, publicly-accessible full text available August 4, 2025
  5. ; ; (, IEEE)
    Free, publicly-accessible full text available July 8, 2025
  6. ; (, IEEE)
    Free, publicly-accessible full text available July 8, 2025
  7. ; ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 20, 2025
  8. ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 13, 2025
  9. ; (, International Conference of Learning Representations)
    Free, publicly-accessible full text available May 11, 2025
  10. ; ; (, IEEE)
    Using a subset of observed network links, high-order link prediction (HOLP) infers missing hyperedges, that is links connecting three or more nodes. HOLP emerges in several applications, but existing approaches have not dealt with the associated predictor’s performance. To overcome this limitation, the present contribution develops a Bayesian approach and the relevant predictive distributions that quantify model uncertainty. Gaussian processes model the dependence of each node to the remaining nodes. These nonparametric models yield predictive distributions, which are fused across nodes by means of a pseudo-likelihood based criterion. Performance is quantified by proper measures of dispersion, which are associated with the predictive distributions. Tests on benchmark datasets demonstrate the benefits of the novel approach. 
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