# Approximating Network Centrality Measures Using Node Embedding and Machine Learning

arXiv: https://arxiv.org/abs/2006.16392 · HTML: https://mlsanity.com/paper/2006.16392/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs](https://mlsanity.com/paper/2310.15865/) (2023)
- [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)
- [Edge Ranking of Graphs in Transportation Networks using a Graph Neural Network (GNN)](https://mlsanity.com/paper/2303.17485/) (2023)
- [Data-driven Intra-Autonomous Systems Graph Generator](https://mlsanity.com/paper/2308.05254/) (2023)
- [CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs](https://mlsanity.com/paper/2506.14122/) (2025)

## Abstract (arXiv)

Extracting information from real-world large networks is a key challenge nowadays. For instance, computing a node centrality may become unfeasible depending on the intended centrality due to its computational cost. One solution is to develop fast methods capable of approximating network centralities. Here, we propose an approach for efficiently approximating node centralities for large networks using Neural Networks and Graph Embedding techniques. Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), uses the adjacency matrix of a graph and a set of features for each node (here, we use only the degree) as input and computes the approximate desired centrality rank for every node. NCA-GE has a time complexity of O(|E|), E being the set of edges of a graph, making it suitable for large networks. NCA-GE also trains pretty fast, requiring only a set of a thousand small synthetic scale-free graphs (ranging from 100 to 1000 nodes each), and it works well for different node centralities, network sizes, and topologies. Finally, we compare our approach to the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input, where we show that the NCA-GE outperforms the former in a variety of scenarios.