Can Degree Correlation Help to Design Resilient Superpeer Networks?

Resilience analysis of the popular P2P networks (like Gnutella) has emerged as an important research issue for the network community. Most of the contemporary studies primarily focused on the estimation of percolation threshold and disruption on the largest connected component due to node removal. However, real-world networks exhibit intrinsically degree-degree correlation, which makes the behavior of a real-world network distinctly different from a random network. We believe that the proper exploitation of the degree-degree correlation information can be helpful in healing the damage caused on P2P systems by the attacks. In order to investigate, we first develop an analytical framework to study the impact of degree-degree correlation on the resilience of real-world networks at different levels (node isolation, network density and component level). Our analysis shows that the Facebook-like network, which exhibits positive degree-degree correlation are much more robust than the negatively correlated network such as Gnutella. We capitalize on this observation and propose a lightweight correlation driven local link-rewiring mechanism that can improve the resilience of a Gnutella-like network against malicious node-perturbations. We substantiate our claims with the help of rigorous simulations on the real-world Gnutella topology and Facebook social graph as well as synthetic network datasets.