Epidemic spreading on multi-relational networks

Abstract

Networks with links representing different relationships have attracted much attention in recent years. Previous studies mostly focused on the analyses of network topology and evolution, multi-relation pattern mining, detection of overlapping communities, and cascading failure. However, epidemic spreading on multi-relation networks remains a largely unexplored area. We propose a binary-relation network model, representing working and friendship relationships, to reveal the effect of multiple relationships on the epidemic spreading. A link representing a closer relationship carries a higher weight. For reactive infection process in a multi-relation network, the threshold of outbreak is suppressed, making the epidemic harder to control. Comparing the networks with different structural heterogeneities such as the Watts-Strogatz (WS), Erdös-Rènyi and Barabási-Albert networks, the WS network is affected most significantly. Interestingly, the relative changes in the thresholds on the three networks are found to be independent of the structure. For contact infection process, an increase in the weight of the closer relationship can raise the outbreak threshold significantly and reduce the prevalence. As the fraction of closer relationship varies, an optimal fraction corresponding to a maximum outbreak threshold and minimum prevalence emerges. With an increase in the weight of the closer relationship, the proportion of links corresponding to the optimal value decreases. Most interestingly, the optimal proportions of closer-relation links on the three networks are almost the same, and thus they are independent of the network topology. This study not only contributes to the better understanding of epidemic spreading dynamics on multi-relation networks, but also provides a new perspective for research on multi-relation networks.