# Multiple propagation paths enhance locating the source of diffusion in complex networks

## TL;DR

This paper introduces a maximum likelihood-based method for locating diffusion sources in complex networks, accounting for multiple shortest paths, which improves accuracy over traditional single-path assumptions.

## Contribution

It presents a novel source estimation technique that considers multiple shortest paths, enhancing accuracy in diffusion source localization in complex networks.

## Key findings

- Up to 1.6 times higher accuracy in synthetic networks
- Addresses overestimation issues in existing methods
- Effective in both synthetic and real networks

## Abstract

We investigate the problem of locating the source of diffusion in complex networks without complete knowledge of nodes' states. Some currently known methods assume the information travels via a single, shortest path, which by assumption is the fastest way. We show that such a method leads to the overestimation of propagation time for synthetic and real networks, where multiple shortest paths as well as longer paths between vertices exist. We propose a new method of source estimation based on maximum likelihood principle, that takes into account existence multiple shortest paths. It shows up to 1.6 times higher accuracy in synthetic and real networks.