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.
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Bagavathi, A., Krishnan, S. (2019). Multi-Net: A Scalable Multiplex Network Embedding Framework. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_10
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DOI: https://doi.org/10.1007/978-3-030-05414-4_10
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