# Permute Me Softly: Learning Soft Permutations for Graph Representations

@article{Nikolentzos2021PermuteMS, title={Permute Me Softly: Learning Soft Permutations for Graph Representations}, author={Giannis Nikolentzos and George Dasoulas and Michalis Vazirgiannis}, journal={IEEE transactions on pattern analysis and machine intelligence}, year={2021}, volume={PP} }

Graph neural networks (GNNs) have recently emerged as a dominant paradigm for machine learning with graphs. Research on GNNs has mainly focused on the family of message passing neural networks (MPNNs). Similar to the Weisfeiler-Leman (WL) test of isomorphism, these models follow an iterative neighborhood aggregation procedure to update vertex representations, and they next compute graph representations by aggregating the representations of the vertices. Although very successful, MPNNs have been…

## 2 Citations

### Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations

- Computer ScienceArXiv
- 2022

A distance function between nodes which is based on the hierarchy produced by the WL algorithm, and a model that learns representations which preserve those distances between nodes is proposed which achieves competitive performance with state-of-the-art models.

### A graph neural network framework for mapping histological topology in oral mucosal tissue

- Computer ScienceBMC Bioinformatics
- 2022

A two stage machine learning pipeline for generating a cell-graph from a digital H &E stained tissue image is shown to predict both low- and high-level histological features in oral mucosal tissue with good accuracy.

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