# Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach

arXiv: https://arxiv.org/abs/2202.06229 · HTML: https://mlsanity.com/paper/2202.06229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Vital nodes identification in complex networks](https://mlsanity.com/paper/1607.01134/) (2016)
- [Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks](https://mlsanity.com/paper/2005.04074/) (2020)
- [Locating influential nodes via dynamics-sensitive centrality](https://mlsanity.com/paper/1504.06672/) (2015)
- [Identifying influential spreaders in complex networks based on gravity formula](https://mlsanity.com/paper/1505.02476/) (2015)
- [Leveraging local h-index to identify and rank influential spreaders in networks](https://mlsanity.com/paper/1708.09532/) (2017)

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

- [Swarm Analytics: Designing Information Markers to Characterise Swarm Systems in Shepherding Contexts](https://mlsanity.com/paper/2208.12386/) (2022)

## Abstract (arXiv)

Vital node identification is the problem of finding nodes of highest importance in complex networks. This problem has crucial applications in various contexts such as viral marketing or controlling the propagation of virus or rumours in real-world networks. Existing approaches for vital node identification mainly focus on capturing the importance of a node through a mathematical expression which directly relates structural properties of the node to its vitality. Although these heuristic approaches have achieved good performance in practice, they have weak adaptability, and their performance is limited to specific settings and certain dynamics. Inspired by the power of machine learning models for efficiently capturing different types of patterns and relations, we propose a machine learning-based, data driven approach for vital node identification. The main idea is to train the model with a small portion of the graph, say 0.5% of the nodes, and do the prediction on the rest of the nodes. The ground-truth vitality for the train data is computed by simulating the SIR diffusion method starting from the train nodes. We use collective feature engineering where each node in the network is represented by incorporating elements of its connectivity, degree and extended coreness. Several machine learning models are trained on the node representations, but the best results are achieved by a Support Vector Regression machine with RBF kernel. The empirical results confirms that the proposed model outperforms state-of-the-art models on a selection of datasets, while it also shows more adaptability to changes in the dynamics parameters.