Approximating Network Centrality Measures Using Node Embedding and Machine Learning

TL;DR

This paper introduces NCA-GE, a neural network-based method that efficiently approximates node centralities in large networks using graph embeddings, significantly reducing computational costs while maintaining accuracy.

Contribution

The paper presents a novel neural network model, NCA-GE, that approximates various node centralities using graph embeddings with linear time complexity, suitable for large-scale networks.

Findings

NCA-GE outperforms existing methods in approximating centrality ranks.

The approach is fast, requiring only small synthetic graphs for training.

It generalizes well across different network sizes and topologies.

Abstract

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.