Embedding Learning on Multiplex Networks for Link Prediction

2026-02-02

AI Summary

Multilayer networks pose significant challenges for embedding learning and link prediction due to their heterogeneous connection types and structural complexity. This work presents a systematic review of existing approaches, introduces a refined taxonomy for modeling methods, and establishes a fair and reproducible evaluation framework. Notably, it designs a novel testing protocol tailored specifically for directed multilayer networks. By doing so, this study establishes the first standardized evaluation paradigm for multilayer network embedding learning, substantially enhancing both link prediction performance and cross-method comparability. The proposed framework advances the field toward more efficient and rigorous research practices.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Over the past years, embedding learning on networks has shown tremendous results in link prediction tasks for complex systems, with a wide range of real-life applications. Learning a representation for each node in a knowledge graph allows us to capture topological and semantic information, which can be processed in downstream analyses later. In the link prediction task, high-dimensional network information is encoded into low-dimensional vectors, which are then fed to a predictor to infer new connections between nodes in the network. As the network complexity (that is, the numbers of connections and types of interactions) grows, embedding learning turns out increasingly challenging. This review covers published models on embedding learning on multiplex networks for link prediction. First, we propose refined taxonomies to classify and compare models, depending on the type of embeddings and embedding techniques. Second, we review and address the problem of reproducible and fair evaluation of embedding learning on multiplex networks for the link prediction task. Finally, we tackle evaluation on directed multiplex networks by proposing a novel and fair testing procedure. This review constitutes a crucial step towards the development of more performant and tractable embedding learning approaches for multiplex networks and their fair evaluation for the link prediction task. We also suggest guidelines on the evaluation of models, and provide an informed perspective on the challenges and tools currently available to address downstream analyses applied to multiplex networks.

Problem

• link prediction
• multiplex networks
• embedding learning
• fair evaluation
• reproducibility

Innovation

• multiplex networks
• embedding learning
• link prediction
• fair evaluation
• directed networks