Towards effective visual analytics on multiplex and multilayer networks
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Abstract (arXiv)
In this article we discuss visualisation strategies for multiplex networks. Since Moreno's early works on network analysis, visualisation has been one of the main ways to understand networks thanks to its ability to summarise a complex structure into a single representation highlighting multiple properties of the data. However, despite the large renewed interest in the analysis of multiplex networks, no study has proposed specialised visualisation approaches for this context and traditional methods are typically applied instead. In this paper we initiate a critical and structured discussion of this topic, and claim that the development of specific visualisation methods for multiplex networks will be one of the main drivers pushing current research results into daily practice.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jan 2015.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 2.
6 papers.
- 2023Privacy-Preserving Community Detection for Locally Distributed Multiple Networkscited by 2
- 2025Identifying Super Spreaders in Multilayer Networkscited by 2
- 2025Multilayer Artificial Benchmark for Community Detection (mABCD)cited by 1
- 2023Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphscited by 0
- 2026T-GINEE: A Tensor-Based Multilayer Graph Representation Learningcited by 0
- 2026Degree-Corrected Joint Matrix Factorization for Multilayer Community Detectioncited by 0
- 2026Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection1 Oct 2026
- 2026T-GINEE: A Tensor-Based Multilayer Graph Representation Learning27 May 2026
- 2025Multilayer Artificial Benchmark for Community Detection (mABCD)14 Jul 2025
- 2025Identifying Super Spreaders in Multilayer Networks27 May 2025
- 2023Privacy-Preserving Community Detection for Locally Distributed Multiple Networks27 Jun 2023
- 2023Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs31 May 2023