# Graph heat mixture model learning

arXiv: https://arxiv.org/abs/1901.08585 · HTML: https://mlsanity.com/paper/1901.08585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

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

- [Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data](https://mlsanity.com/paper/2411.05729/) (2024)

## Abstract (arXiv)

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a priori. In this paper, we argue that this is not always realistic and we introduce a generative model for mixed signals following a heat diffusion process on multiple graphs. We propose an expectation-maximisation algorithm that can successfully separate signals into corresponding groups, and infer multiple graphs that govern their behaviour. We demonstrate the benefits of our method on both synthetic and real data.