# Influence maximization by rumor spreading on correlated networks through community identification

## TL;DR

This paper presents a new, efficient method for selecting initial spreaders to maximize information dissemination in correlated networks, leveraging community detection and degree correlations, with comparable results to greedy algorithms but at lower computational cost.

## Contribution

Introduces a novel, less time-consuming method for influence maximization that accounts for community structure and degree correlations in complex networks.

## Key findings

- Method performs similarly to greedy algorithms in outbreak size.
- Algorithm is significantly faster than traditional greedy approaches.
- Effectiveness depends on degree-degree correlation in networks.

## Abstract

The identification of the minimal set of nodes that maximizes the propagation of information is one of the most relevant problems in network science. In this paper, we introduce a new method to find the set of initial spreaders to maximize the information propagation in complex networks. We evaluate this method in assortative networks and verify that degree-degree correlation plays a fundamental role in the spreading dynamics. Simulation results show that our algorithm is statistically similar, regarding the average size of outbreaks, to the greedy approach in real-world networks. However, our method is much less time consuming than the greedy algorithm.