Suppressing epidemics on networks by exploiting observer nodes

Abstract

To control infection spreading on networks, we investigate the effect of observer nodes that recognize infection in a neighboring node and make the rest of the neighbor nodes immune. We numerically show that random placement of observer nodes works better on networks with clustering than on locally treelike networks, implying that our model is promising for realistic social networks. The efficiency of several heuristic schemes for observer placement is also examined for synthetic and empirical networks. In parallel with numerical simulations of epidemic dynamics, we also show that the effect of observer placement can be assessed by the size of the largest connected component of networks remaining after removing observer nodes and links between their neighboring nodes.

14 pages, 6 figures

References in corpus (4)

• Efficient Immunization Strategies for Computer Networks and Populations
• Random graphs with clustering
• Efficient local strategies for vaccination and network attack
• Performance of networks of artificial neurons: The role of clustering

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