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A Unified Approach to Link Prediction in Collaboration Networks
Un enfoque unificado para la predicción de enlaces en redes de colaboración
DOI:
https://doi.org/10.15446/rce.v48n2.117558Keywords:
Collaboration networks, Exponential random graph model, Graph convolutional network, Word2Vec, Social networks analysis. (en)Redes de colaboración, Modelo exponencial de grafos aleatorios, Red de convolución sobre grafos, Word2Vec, Análisis de redes sociales. (es)
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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 & Welling 2017), and a Word2Vec+MLP model (Word2Vec model combined with a multilayer neural network; Mikolov, Chen, Corrado & Dean 2013 and Goodfellow et al. 2016). The ERGM, grounded in statistical methods, is employed to capture general structural patterns within the network, while the GCN andWord2Vec+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.
Este artículo investiga y compara tres enfoques para la predicción de enlaces en redes de colaboración: un ERGM (Exponential Random Graph Model; Robins et al., 2007), una GCN (Graph Convolutional Network; Kipf & Welling, 2017) y un modelo Word2Vec+MLP (modelo Word2Vec combinado con una red neuronal multicapa; Mikolov, Chen, Corrado & Dean (2013), y Goodfellow et al. (2016)). El ERGM, basado en métodos estadísticos, se emplea para capturar patrones estructurales generales dentro de la red, mientras que los modelos GCN y Word2Vec+MLP utilizan técnicas de aprendizaje profundo para aprender representaciones estructurales adaptativas de los nodos y sus relaciones. El desempeño predictivo de los modelos se evalúa mediante extensos ejercicios de simulación con validación cruzada, utilizando métricas basadas en la curva característica operativa del receptor (ROC). Los resultados muestran claramente la superioridad de los enfoques de aprendizaje automático en la predicción de enlaces, particularmente en redes grandes, donde los modelos tradicionales como el ERGM presentan limitaciones en escalabilidad y en la capacidad de capturar complejidades inherentes. Estos hallazgos resaltan los posibles beneficios de integrar técnicas de modelado estadístico con métodos de aprendizaje profundo para analizar redes complejas, proporcionando un marco más robusto y efectivo para futuras investigaciones en este campo.
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