Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach

TL;DR

This paper introduces CUL, an unsupervised deep learning approach for efficiently identifying high eigenvector centrality nodes in large networks, outperforming supervised methods in accuracy and speed.

Contribution

The paper presents a novel unsupervised encoder-decoder framework for estimating eigenvector centrality, addressing scalability and label scarcity issues in real-world networks.

Findings

CUL outperforms supervised methods in accuracy for high EC node identification.

CUL is faster and more scalable than traditional EC computation methods.

Even with minimal training data, CUL achieves superior results.

Abstract

The existing methods to calculate the Eigenvector Centrality(EC) tend to not be robust enough for determination of EC in low time complexity or not well-scalable for large networks, hence rendering them practically unreliable/ computationally expensive. So, it is of the essence to develop a method that is scalable in low computational time. Hence, we propose a deep learning model for the identification of nodes with high Eigenvector Centrality. There have been a few previous works in identifying the high ranked nodes with supervised learning methods, but in real-world cases, the graphs are not labelled and hence deployment of supervised learning methods becomes a hazard and its usage becomes impractical. So, we devise CUL(Centrality with Unsupervised Learning) method to learn the relative EC scores in a network in an unsupervised manner. To achieve this, we develop an Encoder-Decoder based framework that maps the nodes to their respective estimated EC scores. Extensive experiments were conducted on different synthetic and real-world networks. We compared CUL against a baseline supervised method for EC estimation similar to some of the past works. It was observed that even with training on a minuscule number of training datasets, CUL delivers a relatively better accuracy score when identifying the higher ranked nodes than its supervised counterpart. We also show that CUL is much faster and has a smaller runtime than the conventional baseline method for EC computation. The code is available at https://github.com/codexhammer/CUL.