# A Recurrent Graph Neural Network for Multi-Relational Data

arXiv: https://arxiv.org/abs/1811.02061 · HTML: https://mlsanity.com/paper/1811.02061/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [Graph Neural Networks Designed for Different Graph Types: A Survey](https://mlsanity.com/paper/2204.03080/) (2022)
- [GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features](https://mlsanity.com/paper/2211.12482/) (2022)
- [On the Correspondence Between Monotonic Max-Sum GNNs and Datalog](https://mlsanity.com/paper/2305.18015/) (2023)
- [Time-varying Signals Recovery via Graph Neural Networks](https://mlsanity.com/paper/2302.11313/) (2023)
- [Relational Graph Convolutional Networks Do Not Learn Sound Rules](https://mlsanity.com/paper/2408.10261/) (2024)
- [Bespoke multiresolution analysis of graph signals](https://mlsanity.com/paper/2507.19181/) (2025)
- [Sound Logical Explanations for Mean Aggregation Graph Neural Networks](https://mlsanity.com/paper/2511.11593/) (2025)
- [Higher-order Sparse Convolutions in Graph Neural Networks](https://mlsanity.com/paper/2302.10505/) (2023)
- [BHGNN-RT: Network embedding for directed heterogeneous graphs](https://mlsanity.com/paper/2311.14404/) (2023)
- [RCCFormer: A Robust Crowd Counting Network Based on Transformer](https://mlsanity.com/paper/2504.04935/) (2025)
- [Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition](https://mlsanity.com/paper/2206.04855/) (2022)
- [Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching](https://mlsanity.com/paper/2404.12724/) (2024)
- [Logical Expressivity and Explanations for Monotonic GNNs with Scoring Functions](https://mlsanity.com/paper/2508.14091/) (2025)

## Abstract (arXiv)

The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering. In this paper, we introduce a graph recurrent neural network (GRNN) for scalable semi-supervised learning from multi-relational data. Key aspects of the novel GRNN architecture are the use of multi-relational graphs, the dynamic adaptation to the different relations via learnable weights, and the consideration of graph-based regularizers to promote smoothness and alleviate over-parametrization. Our ultimate goal is to design a powerful learning architecture able to: discover complex and highly non-linear data associations, combine (and select) multiple types of relations, and scale gracefully with respect to the size of the graph. Numerical tests with real data sets corroborate the design goals and illustrate the performance gains relative to competing alternatives.