# Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems

arXiv: https://arxiv.org/abs/2309.13229 · HTML: https://mlsanity.com/paper/2309.13229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 60 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Attributed Social Network Embedding](https://mlsanity.com/paper/1705.04969/) (2017)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Measuring Quadrangle Formation in Complex Networks](https://mlsanity.com/paper/2011.10763/) (2020)
- [Epidemic spreading on complex networks with community structures](https://mlsanity.com/paper/1611.06092/) (2016)
- [Interacting spreading processes in multilayer networks](https://mlsanity.com/paper/1903.05932/) (2019)
- [Simulation and Augmentation of Social Networks for Building Deep Learning Models](https://mlsanity.com/paper/1905.09087/) (2019)
- [Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules](https://mlsanity.com/paper/2308.11034/) (2023)
- [Epidemic Spreading on Weighted Complex Networks](https://mlsanity.com/paper/1308.4014/) (2013)

## Built on by (most cited first, 1 of 1)

- [How the use of feature selection methods influences the efficiency and accuracy of complex network simulations](https://mlsanity.com/paper/2412.01096/) (2024)

## Abstract (arXiv)

Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes' features and feature differences. Our empirical analysis builds DT-CNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. We also investigate their respective disaster resilience to an epidemic outbreak starting from the most popular node. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure and the speed of the epidemic outbreak, requiring various mitigation policies targeted at different people.