# Machine Learning in Network Centrality Measures: Tutorial and Outlook

arXiv: https://arxiv.org/abs/1810.11760 · HTML: https://mlsanity.com/paper/1810.11760/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review](https://mlsanity.com/paper/2104.04958/) (2021)
- [Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs](https://mlsanity.com/paper/2310.15865/) (2023)
- [Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks](https://mlsanity.com/paper/2403.04977/) (2024)
- [Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach](https://mlsanity.com/paper/2111.05264/) (2021)
- [Can one hear the position of nodes?](https://mlsanity.com/paper/2211.06325/) (2022)
- [HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking](https://mlsanity.com/paper/2603.11472/) (2026)
- [Topology-induced Operators Reveal Complementary Graph Representations without Training](https://mlsanity.com/paper/2609.08152/) (2026)

## Abstract (arXiv)

Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore are a robust, effective alternative in real-world applications. The methodology and proposed machine learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.