# Propagation on Multi-relational Graphs for Node Regression

arXiv: https://arxiv.org/abs/2110.08185 · HTML: https://mlsanity.com/paper/2110.08185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 15 Oct 2021.

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

- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Boost then Convolve: Gradient Boosting Meets Graph Neural Networks](https://mlsanity.com/paper/2101.08543/) (2021)
- [Stationary signal processing on graphs](https://mlsanity.com/paper/1601.02522/) (2016)
- [Spatio-Temporal Deep Graph Infomax](https://mlsanity.com/paper/1904.06316/) (2019)
- [Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation](https://mlsanity.com/paper/2011.05301/) (2020)
- [Multilayer Networks in a Nutshell](https://mlsanity.com/paper/1804.03488/) (2018)
- [The multilayer connectome of Caenorhabditis elegans](https://mlsanity.com/paper/1608.08793/) (2016)
- [Multilayer networks: metrics and spectral properties](https://mlsanity.com/paper/1504.05567/) (2015)

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

- [On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features](https://mlsanity.com/paper/2111.12128/) (2021)

## Abstract (arXiv)

Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the current relational learning research, we go one step further and focus on node regression problem on multi-relational graphs. We take inspiration from the well-known label propagation algorithm aiming at completing categorical features across a simple graph and propose a novel propagation framework for completing missing continuous features at the nodes of a multi-relational and directed graph. Our multi-relational propagation algorithm is composed of iterative neighborhood aggregations which originate from a relational local generative model. Our findings show the benefit of exploiting the multi-relational structure of the data in several node regression scenarios in different settings.