Graph link prediction in computer networks using Poisson matrix factorisation
DOI10.1214/21-AOAS1540zbMath1498.62349arXiv2001.09456OpenAlexW3002138967WikidataQ114060488 ScholiaQ114060488MaRDI QIDQ2170383
Melissa J. M. Turcotte, Nicholas A. Heard, Francesco Sanna Passino
Publication date: 5 September 2022
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2001.09456
dynamic networksvariational inferenceanomaly detectionnew link predictionPoisson matrix factorisationstatistical cybersecurity
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Social networks; opinion dynamics (91D30) Applications of statistics (62P99)
Cites Work
- Unnamed Item
- Unnamed Item
- On the identifiability of Bayesian factor analytic models
- Modeling item-item similarities for personalized recommendations on Yahoo! front page
- A review of dynamic network models with latent variables
- Flexible low-rank statistical modeling with missing data and side information
- On Bayesian new edge prediction and anomaly detection in computer networks
- Variational Bayesian inference for the latent position cluster model for network data
- Latent Space Approaches to Social Network Analysis
- Statistical inference on random dot product graphs: a survey
- Content‐boosted matrix factorization techniques for recommender systems
- A Survey of Statistical Network Models
- Bayesian latent variable models for mixed discrete outcomes
- Latent Space Models for Dynamic Networks
- Statistical methods for network surveillance
- Bilinear Mixed-Effects Models for Dyadic Data
This page was built for publication: Graph link prediction in computer networks using Poisson matrix factorisation