# Machine Learning Model for Sparse PCM Completion

arXiv: https://arxiv.org/abs/2601.04366 · HTML: https://mlsanity.com/paper/2601.04366/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Spectral Ranking using Seriation](https://mlsanity.com/paper/1406.5370/) (2014)
- [Inconsistency thresholds for incomplete pairwise comparison matrices](https://mlsanity.com/paper/2102.10558/) (2021)

## Abstract (arXiv)

In this paper, we propose a machine learning model for sparse pairwise comparison matrices (PCMs), combining classical PCM approaches with graph-based learning techniques. Numerical results are provided to demonstrate the effectiveness and scalability of the proposed method.