MultiNet: A Scalable Multilayer Network Embedding Framework

TL;DR

MultiNet is a scalable embedding framework designed for multiplex networks, effectively capturing neighborhood information across layers, demonstrating superior performance and robustness on real-world datasets, and enabling complex network reconstruction tasks.

Contribution

MultiNet introduces a fast, scalable multilayer network embedding method using four random walk strategies, improving robustness and performance over existing models.

Findings

Outperforms state-of-the-art models on four real-world datasets.

Effectively preserves neighborhood properties across network layers.

Enables complex network reconstruction tasks.

Abstract

Representation learning of networks has witnessed significant progress in recent times. Such representations have been effectively used for classic network-based machine learning tasks like node classification, link prediction, and network alignment. However, very few methods focus on capturing representations for multiplex or multilayer networks, which are more accurate and detailed representations of complex networks. In this work, we propose Multi-Net a fast and scalable embedding technique for multiplex networks. Multi-Net, effectively maps nodes to a lower-dimensional space while preserving its neighborhood properties across all the layers. We utilize four random walk strategies in our unified network embedding model, thus making our approach more robust than existing state-of-the-art models. We demonstrate superior performance of Multi-Net on four real-world datasets from different domains. In particular, we highlight the uniqueness of Multi-Net by leveraging it for the complex task of network reconstruction.