# Weighted Multiplex Networks

arXiv: https://arxiv.org/abs/1312.6720 · HTML: https://mlsanity.com/paper/1312.6720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2013.

## Built on by (most cited first, 4 of 4)

- [Community Detection in Weighted Multilayer Networks with Ambient Noise](https://mlsanity.com/paper/2103.00486/) (2021)
- [AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands](https://mlsanity.com/paper/2112.13168/) (2021)
- [Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction](https://mlsanity.com/paper/2307.08877/) (2023)
- [Looking Through Glass Box](https://mlsanity.com/paper/2603.06272/) (2026)

## Abstract (arXiv)

One of the most important challenges in network science is to quantify the information encoded in complex network structures. Disentangling randomness from organizational principles is even more demanding when networks have a multiplex nature. Multiplex networks are multilayer systems of N nodes that can be linked in multiple interacting and co-evolving layers. In these networks, relevant information might not be captured if the single layers were analyzed separately. Here we demonstrate that such partial analysis of layers fails to capture significant correlations between weights and topology of complex multiplex networks. To this end, we study two weighted multiplex co-authorship and citation networks involving the authors included in the American Physical Society. We show that in these networks weights are strongly correlated with multiplex structure, and provide empirical evidence in favor of the advantage of studying weighted measures of multiplex networks, such as multistrength and the inverse multiparticipation ratio. Finally, we introduce a theoretical framework based on the entropy of multiplex ensembles to quantify the information stored in multiplex networks that would remain undetected if the single layers were analyzed in isolation.