# Matrix Factorization for Inferring Associations and Missing Links

arXiv: https://arxiv.org/abs/2503.04680 · HTML: https://mlsanity.com/paper/2503.04680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 66 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [Recent Developments in Boolean Matrix Factorization](https://mlsanity.com/paper/2012.03127/) (2020)
- [On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations](https://mlsanity.com/paper/1607.00084/) (2016)
- [Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection](https://mlsanity.com/paper/2309.06643/) (2023)
- [A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization](https://mlsanity.com/paper/2006.12402/) (2020)
- [Confidence from Invariance to Image Transformations](https://mlsanity.com/paper/1804.00657/) (2018)
- [Boolean Matrix Factorization via Nonnegative Auxiliary Optimization](https://mlsanity.com/paper/2106.04708/) (2021)
- [An FCA-based Boolean Matrix Factorisation for Collaborative Filtering](https://mlsanity.com/paper/1310.4366/) (2013)
- [Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model](https://mlsanity.com/paper/1904.03099/) (2019)

## Built on by (most cited first, 1 of 1)

- [Topic Modeling and Link-Prediction for Material Property Discovery](https://mlsanity.com/paper/2507.06139/) (2025)

## Abstract (arXiv)

Missing link prediction is a method for network analysis, with applications in recommender systems, biology, social sciences, cybersecurity, information retrieval, and Artificial Intelligence (AI) reasoning in Knowledge Graphs. Missing link prediction identifies unseen but potentially existing connections in a network by analyzing the observed patterns and relationships. In proliferation detection, this supports efforts to identify and characterize attempts by state and non-state actors to acquire nuclear weapons or associated technology - a notoriously challenging but vital mission for global security. Dimensionality reduction techniques like Non-Negative Matrix Factorization (NMF) and Logistic Matrix Factorization (LMF) are effective but require selection of the matrix rank parameter, that is, of the number of hidden features, k, to avoid over/under-fitting. We introduce novel Weighted (WNMFk), Boolean (BNMFk), and Recommender (RNMFk) matrix factorization methods, along with ensemble variants incorporating logistic factorization, for link prediction. Our methods integrate automatic model determination for rank estimation by evaluating stability and accuracy using a modified bootstrap methodology and uncertainty quantification (UQ), assessing prediction reliability under random perturbations. We incorporate Otsu threshold selection and k-means clustering for Boolean matrix factorization, comparing them to coordinate descent-based Boolean thresholding. Our experiments highlight the impact of rank k selection, evaluate model performance under varying test-set sizes, and demonstrate the benefits of UQ for reliable predictions using abstention. We validate our methods on three synthetic datasets (Boolean and uniformly distributed) and benchmark them against LMF and symmetric LMF (symLMF) on five real-world protein-protein interaction networks, showcasing an improved prediction performance.