# Systematic comparison between methods for the detection of influential spreaders in complex networks

## TL;DR

This paper systematically compares various heuristic methods for identifying influential spreaders in large networks, finding that simple metrics like degree and closeness centrality perform nearly as well as optimal greedy algorithms.

## Contribution

It provides a comprehensive evaluation of topological heuristics against optimal solutions in real-world networks, clarifying their effectiveness for influence maximization.

## Key findings

- Simple metrics like degree and closeness centrality perform close to optimal.
- Heuristic methods are effective for large-scale influence maximization.
- Greedy algorithms serve as a baseline for performance comparison.

## Abstract

Influence maximization is the problem of finding the set of nodes of a network that maximizes the size of the outbreak of a spreading process occurring on the network. Solutions to this problem are important for strategic decisions in marketing and political campaigns. The typical setting consists in the identification of small sets of initial spreaders in very large networks. This setting makes the optimization problem computationally infeasible for standard greedy optimization algorithms that account simultaneously for information about network topology and spreading dynamics, leaving space only to heuristic methods based on the drastic approximation of relying on the geometry of the network alone. The literature on the subject is plenty of purely topological methods for the identification of influential spreaders in networks. However, it is unclear how far these methods are from being optimal. Here, we perform a systematic test of the performance of a multitude of heuristic methods for the identification of influential spreaders. We quantify the performance of the various methods on a corpus of 100 real-world networks; the corpus consists of networks small enough for the application of greedy optimization so that results from this algorithm are used as the baseline needed for the analysis of the performance of the other methods on the same corpus of networks. We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings....