Contact Process with Exogenous Infection and the Scaled SIS Process
June Zhang, Jos\'e M.F. Moura

TL;DR
This paper analyzes the long-term behavior of an extended contact process with exogenous infection on networks, showing that it can be approximated by a scaled SIS process for certain parameters, enabling efficient susceptibility analysis.
Contribution
It demonstrates that the equilibrium distribution of the extended contact process can be approximated by a scaled SIS process on arbitrary networks, facilitating polynomial-time susceptibility analysis.
Findings
Equilibrium distribution of extended contact process approximated by scaled SIS process.
Numerical simulations confirm the approximation accuracy.
Allows efficient identification of susceptible agents and substructures.
Abstract
Propagation of contagion in networks depends on the graph topology. This paper is concerned with studying the time-asymptotic behavior of the extended contact processes on static, undirected, finite-size networks. This is a contact process with nonzero exogenous infection rate (also known as the {\epsilon}-SIS, {\epsilon} susceptible-infected-susceptible, model [1]). The only known analytical characterization of the equilibrium distribution of this process is for complete networks. For large networks with arbitrary topology, it is infeasible to numerically solve for the equilibrium distribution since it requires solving the eigenvalue-eigenvector problem of a matrix that is exponential in N , the size of the network. We show that, for a certain range of the network process parameters, the equilibrium distribution of the extended contact process on arbitrary, finite-size networks is well…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Stochastic processes and statistical mechanics
