# Epidemic processes in complex networks

arXiv: https://arxiv.org/abs/1408.2701 · HTML: https://mlsanity.com/paper/1408.2701/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2014.

## Built on by (most cited first, 30 of 58)

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- [From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration](https://mlsanity.com/paper/2603.04474/) (2026)
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- [Estimating the State of Epidemics Spreading with Graph Neural Networks](https://mlsanity.com/paper/2105.05060/) (2021)
- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [A Bayesian generative neural network framework for epidemic inference problems](https://mlsanity.com/paper/2111.03383/) (2021)
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- [Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression](https://mlsanity.com/paper/2411.06833/) (2024)
- [Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak](https://mlsanity.com/paper/2502.10786/) (2025)
- [PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling](https://mlsanity.com/paper/2602.06030/) (2026)
- [DynaTrust: Defending Multi-Agent Systems Against Sleeper Agents via Dynamic Trust Graphs](https://mlsanity.com/paper/2603.15661/) (2026)
- [Fair Disaster Containment via Graph-Cut Problems](https://mlsanity.com/paper/2106.05424/) (2021)
- [A Direct Approximation of AIXI Using Logical State Abstractions](https://mlsanity.com/paper/2210.06917/) (2022)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [A Graph Dynamics Prior for Relational Inference](https://mlsanity.com/paper/2306.06041/) (2023)
- [Influential Simplices Mining via Simplicial Convolutional Network](https://mlsanity.com/paper/2307.05841/) (2023)
- [Neural Symbolic Regression of Complex Network Dynamics](https://mlsanity.com/paper/2410.11185/) (2024)
- [Contagion Networks: Evaluator Preference Propagation in Multi-Agent LLM Systems](https://mlsanity.com/paper/2606.20493/) (2026)
- [A Reputation Game Simulation: Emergent Social Phenomena from Information Theory](https://mlsanity.com/paper/2106.05414/) (2021)
- [Simulation of emergence in artificial societies: a practical model-based approach with the EB-DEVS formalism](https://mlsanity.com/paper/2110.08170/) (2021)
- [Transmission Neural Networks: From Virus Spread Models to Neural Networks](https://mlsanity.com/paper/2208.03616/) (2022)
- [Privacy Preserving Reinforcement Learning for Population Processes](https://mlsanity.com/paper/2406.17649/) (2024)
- [Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study](https://mlsanity.com/paper/2503.12813/) (2025)
- [Cyber-Financial Contagion: Modeling the Propagation of an AI Vendor Compromise Through the Banking System](https://mlsanity.com/paper/2609.10350/) (2026)
- [Influence Estimation and Maximization via Neural Mean-Field Dynamics](https://mlsanity.com/paper/2106.02608/) (2021)
- [An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs](https://mlsanity.com/paper/2112.10953/) (2021)

## Abstract (arXiv)

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.