# The Multiple Random Dot Product Graph Model

arXiv: https://arxiv.org/abs/1811.12172 · HTML: https://mlsanity.com/paper/1811.12172/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model](https://mlsanity.com/paper/2209.13762/) (2022)
- [Clustered Graph Matching for Label Recovery and Graph Classification](https://mlsanity.com/paper/2205.03486/) (2022)
- [Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings](https://mlsanity.com/paper/2409.17544/) (2024)

## Abstract (arXiv)

Data in the form of graphs, or networks, arise naturally in a number of contexts; examples include social networks and biological networks. We are often faced with the availability of multiple graphs on a single set of nodes. In this article, we propose the multiple random dot product graph model for this setting. Our proposed model leads naturally to an optimization problem, which we solve using an efficient alternating minimization approach. We further use this model as the basis for a new test for the hypothesis that the graphs come from a single distribution, versus the alternative that they are drawn from different distributions. We evaluate the performance of both the fitting algorithm and the hypothesis test in several simulation settings, and demonstrate empirical improvement over existing approaches. We apply these new approaches to a Wikipedia data set and a C. elegans data set.