# Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2202.09363 · HTML: https://mlsanity.com/paper/2202.09363/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 69 of its 372 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 30 of 69)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://mlsanity.com/paper/2005.07496/) (2020)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [GMNN: Graph Markov Neural Networks](https://mlsanity.com/paper/1905.06214/) (2019)
- [A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale](https://mlsanity.com/paper/2012.05841/) (2020)
- [A Tutorial on Learning With Bayesian Networks](https://mlsanity.com/paper/2002.00269/) (2020)
- [Spectral redemption: clustering sparse networks](https://mlsanity.com/paper/1306.5550/) (2013)
- [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)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks](https://mlsanity.com/paper/1901.09165/) (2019)
- [Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification](https://mlsanity.com/paper/1708.02286/) (2017)
- [Clustering and Community Detection in Directed Networks: A Survey](https://mlsanity.com/paper/1308.0971/) (2013)
- [Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2104.07917/) (2021)
- [Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction](https://mlsanity.com/paper/2006.01963/) (2020)
- [Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2010.05313/) (2020)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Community detection in node-attributed social networks: a survey](https://mlsanity.com/paper/1912.09816/) (2019)
- [Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures](https://mlsanity.com/paper/1312.7857/) (2013)
- [Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning](https://mlsanity.com/paper/2011.06296/) (2020)
- [Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions](https://mlsanity.com/paper/2011.02833/) (2020)

## Built on by (most cited first, 8 of 8)

- [Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules](https://mlsanity.com/paper/2308.11034/) (2023)
- [Bridging the Reality Gap in Digital Twins with Context-Aware, Physics-Guided Deep Learning](https://mlsanity.com/paper/2505.11847/) (2025)
- [Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems](https://mlsanity.com/paper/2309.13229/) (2023)
- [Digital Twin-Native AI-Driven Service Architecture for Industrial Networks](https://mlsanity.com/paper/2311.14532/) (2023)
- [From Cubes to Networks: Fast Generic Model for Synthetic Networks Generation](https://mlsanity.com/paper/2211.02811/) (2022)
- [AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities](https://mlsanity.com/paper/2401.16449/) (2024)
- [The Role and Applications of Airport Digital Twin in Cyberattack Protection during the Generative AI Era](https://mlsanity.com/paper/2408.05248/) (2024)
- [Deep Reinforcement Learning for Digital Twin-Oriented Complex Networked Systems](https://mlsanity.com/paper/2411.06148/) (2024)

## Abstract (arXiv)

This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to assess their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. We also identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques.