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MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction.

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IEEE Transactions on Neural Networks and Learning Systems

|May 24, 2023

Summary

This summary is machine-generated.

MultiplexSAGE embeds multiplex networks by generalizing GraphSAGE, accurately reconstructing both intra-layer and inter-layer connections. Graph density and link randomness significantly impact embedding quality in these complex structures.

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Area of Science:

Graph representation learning
Network science
Machine learning

Background:

Most graph representation learning focuses on single-layer graphs.
Existing multilayer network embedding methods often assume known inter-layer links, limiting applicability.

Purpose of the Study:

To propose MultiplexSAGE, a novel algorithm for embedding multiplex networks.
To generalize the GraphSAGE algorithm for multilayer network representation learning.

Main Methods:

Developed MultiplexSAGE, a generalization of GraphSAGE.
Applied the algorithm to multiplex networks to learn embeddings.
Conducted comprehensive experimental analysis to evaluate performance.

Main Results:

MultiplexSAGE successfully reconstructs both intra-layer and inter-layer connectivity.
The proposed method outperforms existing competing approaches.
Graph density and link randomness were identified as key factors influencing embedding quality.

Conclusions:

MultiplexSAGE offers a flexible approach to embedding multiplex networks without prior knowledge of inter-layer links.
The findings highlight the importance of network structure in graph embedding performance.