# Schemes of Propagation Models and Source Estimators for Rumor Source Detection in Online Social Networks: A Short Survey of a Decade of Research

arXiv: https://arxiv.org/abs/2101.00753 · HTML: https://mlsanity.com/paper/2101.00753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [Catch'Em All: Locating Multiple Diffusion Sources in Networks with Partial Observations](https://mlsanity.com/paper/1611.06963/) (2016)
- [Finding Patient Zero: Learning Contagion Source with Graph Neural Networks](https://mlsanity.com/paper/2006.11913/) (2020)
- [Rooting out the Rumor Culprit from Suspects](https://mlsanity.com/paper/1301.6312/) (2013)
- [Effector Detection in Social Networks](https://mlsanity.com/paper/1612.03864/) (2016)
- [Finding an infection source under the SIS model](https://mlsanity.com/paper/1309.3874/) (2013)
- [Rumor Source Detection under Querying with Untruthful Answers](https://mlsanity.com/paper/1711.05496/) (2017)

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

- [Sequential Attention Source Identification Based on Feature Representation](https://mlsanity.com/paper/2306.15886/) (2023)

## Abstract (arXiv)

Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network. Diffusion model is arguably considered as a very important and challengeable factor for source detection in networks but it is less studied. This paper provides an overview of three representative schemes of Independent Cascade-based, Epidemic-based, and Learning-based to model the patterns of rumor propagation as well as three major schemes of estimators for rumor sources since its inception a decade ago.