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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.12730 |
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| _version_ | 1866916137224110080 |
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| author | Kaiser, Daniel Patwardhan, Siddharth Kim, Minsuk Radicchi, Filippo |
| author_facet | Kaiser, Daniel Patwardhan, Siddharth Kim, Minsuk Radicchi, Filippo |
| contents | Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper of network science. Here, we develop a machine learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_12730 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reconstruction of multiplex networks via graph embeddings Kaiser, Daniel Patwardhan, Siddharth Kim, Minsuk Radicchi, Filippo Physics and Society Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper of network science. Here, we develop a machine learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques. |
| title | Reconstruction of multiplex networks via graph embeddings |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2310.12730 |