# Deep Identification of Propagation Trees

arXiv: https://arxiv.org/abs/2503.00646 · HTML: https://mlsanity.com/paper/2503.00646/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2025.

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

- [Toward World Models for Epidemiology](https://mlsanity.com/paper/2604.09519/) (2026)
- [Multimodal Representation Learning Conditioned on Semantic Relations](https://mlsanity.com/paper/2508.17497/) (2025)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

Understanding propagation structures in graph diffusion processes, such as epidemic spread or misinformation diffusion, is a fundamental yet challenging problem. While existing methods primarily focus on source localization, they cannot reconstruct the underlying propagation trees i.e., "who infected whom", which are substantial for tracking the propagation pathways and investigate diffusion mechanisms. In this work, we propose Deep Identification of Propagation Trees (DIPT), a probabilistic framework that infers propagation trees from observed diffused states. DIPT models local influence strengths between nodes and leverages an alternating optimization strategy to jointly learn the diffusion mechanism and reconstruct the propagation structure. Extensive experiments on five real-world datasets demonstrate the effectiveness of DIPT in accurately reconstructing propagation trees.