Quantifying layer similarity in multiplex networks: a systematic study
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Abstract (arXiv)
Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 2.
5 papers.
- 2021ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Networkcited by 8
- 2024Identifying Key Nodes for the Influence Spread using a Machine Learning Approachcited by 1
- 2025Multilayer Artificial Benchmark for Community Detection (mABCD)cited by 1
- 2022A cost-based multi-layer network approach for the discovery of patient phenotypescited by 0
- 2025Multimodal Coordinated Online Behavior: Trade-offs and Strategiescited by 0
- 2025Multimodal Coordinated Online Behavior: Trade-offs and Strategies16 Jul 2025
- 2025Multilayer Artificial Benchmark for Community Detection (mABCD)14 Jul 2025
- 2024Identifying Key Nodes for the Influence Spread using a Machine Learning Approach2 Dec 2024
- 2022A cost-based multi-layer network approach for the discovery of patient phenotypes19 Sep 2022
- 2021ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network20 Feb 2021