# Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules

arXiv: https://arxiv.org/abs/2308.11034 · HTML: https://mlsanity.com/paper/2308.11034/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://mlsanity.com/paper/2005.07496/) (2020)
- [N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification](https://mlsanity.com/paper/1802.08888/) (2018)
- [GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction](https://mlsanity.com/paper/1812.04206/) (2018)
- [Attributed Social Network Embedding](https://mlsanity.com/paper/1705.04969/) (2017)
- [Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction](https://mlsanity.com/paper/2006.01963/) (2020)
- [DELTACON: A Principled Massive-Graph Similarity Function](https://mlsanity.com/paper/1304.4657/) (2013)
- [Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edges](https://mlsanity.com/paper/1607.07330/) (2016)
- [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)
- [Graph Based Link Prediction between Human Phenotypes and Genes](https://mlsanity.com/paper/2105.11989/) (2021)
- [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)
- [Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds](https://mlsanity.com/paper/2212.11611/) (2022)

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

- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems](https://mlsanity.com/paper/2309.13229/) (2023)
- [Deep Reinforcement Learning for Digital Twin-Oriented Complex Networked Systems](https://mlsanity.com/paper/2411.06148/) (2024)
- [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)

This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.