Performance analysis of the general packet radio service
Computer Networks: The International Journal of Computer and Telecommunications Networking
Performance Analysis of Communications Networks and Systems
Performance Analysis of Communications Networks and Systems
IEEE/ACM Transactions on Networking (TON)
A taxonomy of biologically inspired research in computer networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Graph Spectra for Complex Networks
Graph Spectra for Complex Networks
The N-intertwined SIS epidemic network model
Computing - Special Issue on Bio inspired Computing
A Generalized Influence Model for Networked Stochastic Automata
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Spatial-temporal modeling of malware propagation in networks
IEEE Transactions on Neural Networks
Interacting viruses in networks: can both survive?
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Parallel and Distributed Systems
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Mean-field deterministic epidemic models have been successful in uncovering several important dynamic properties of stochastic epidemic spreading processes over complex networks. In particular, individual-based epidemic models isolate the impact of the network topology on spreading dynamics. In this paper, the existing models are generalized to develop a class of models that includes the spreading process in multilayer complex networks. We provide a detailed description of the stochastic process at the agent level where the agents interact through different layers, each represented by a graph. The set of differential equations that describes the time evolution of the state occupancy probabilities has an exponentially growing state-space size in terms of the number of the agents. Based on a mean-field type approximation, we developed a set of nonlinear differential equations that has linearly growing state-space size. We find that the latter system, referred to as the generalized epidemic mean-field (GEMF) model, has a simple structure characterized by the elements of the adjacency matrices of the network layers and the Laplacian matrices of the transition rate graphs. Finally, we present several examples of epidemic models, including spreading of virus and information in computer networks and spreading of multiple pathogens in a host population.