An unified approach to link prediction in collaboration networks

TL;DR

This paper compares three link prediction methods in collaboration networks, demonstrating that deep learning models outperform traditional statistical approaches, especially in large, complex networks.

Contribution

It introduces a unified comparison of ERGM, GCN, and Word2Vec+MLP models for link prediction, highlighting the advantages of deep learning techniques.

Findings

Deep learning models outperform ERGM in large networks

Machine learning approaches show higher predictive accuracy

Traditional ERGM has scalability limitations

Abstract

This article investigates and compares three approaches to link prediction in colaboration networks, namely, an ERGM (Exponential Random Graph Model; Robins et al. 2007), a GCN (Graph Convolutional Network; Kipf and Welling 2017), and a Word2Vec+MLP model (Word2Vec model combined with a multilayer neural network; Mikolov et al. 2013a and Goodfellow et al. 2016). The ERGM, grounded in statistical methods, is employed to capture general structural patterns within the network, while the GCN and Word2Vec+MLP models leverage deep learning techniques to learn adaptive structural representations of nodes and their relationships. The predictive performance of the models is assessed through extensive simulation exercises using cross-validation, with metrics based on the receiver operating characteristic curve. The results clearly show the superiority of machine learning approaches in link prediction, particularly in large networks, where traditional models such as ERGM exhibit limitations in scalability and the ability to capture inherent complexities. These findings highlight the potential benefits of integrating statistical modeling techniques with deep learning methods to analyze complex networks, providing a more robust and effective framework for future research in this field.