# Epidemic spreading on complex networks with general degree and weight distributions

arXiv: https://arxiv.org/abs/1407.0774 · HTML: https://mlsanity.com/paper/1407.0774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2014.

## Built on by (most cited first, 2 of 2)

- [DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks](https://mlsanity.com/paper/2201.02757/) (2022)
- [How the use of feature selection methods influences the efficiency and accuracy of complex network simulations](https://mlsanity.com/paper/2412.01096/) (2024)

## Abstract (arXiv)

The spread of disease on complex networks has attracted widely attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex network with the strong heterogeneous degree and weight distributions, this approach is worked. We then propose an edge-weight based removal strategy with different biases, and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.