The Multiple Instances of Node Centrality and their Implications on the Vulnerability of ISP Networks

TL;DR

This paper surveys various node centrality indices, analyzes their consistency in ranking ISP network nodes, and evaluates their implications on network vulnerability and robustness.

Contribution

It provides an exhaustive categorization of centrality indices and assesses their effectiveness in predicting network vulnerability through empirical analysis.

Findings

Top-k overlap better predicts impact on network vulnerability than full ranking correlation.

Degree centrality closely approximates global indices in terms of traffic capacity impact.

Local degree centrality's effectiveness in connectivity varies with topology.

Abstract

The position of the nodes within a network topology largely determines the level of their involvement in various networking functions. Yet numerous node centrality indices, proposed to quantify how central individual nodes are in this respect, yield very different views of their relative significance. Our first contribution in this paper is then an exhaustive survey and categorization of centrality indices along several attributes including the type of information (local vs. global) and processing complexity required for their computation. We next study the seven most popular of those indices in the context of Internet vulnerability to address issues that remain under-explored in literature so far. First, we carry out a correlation study to assess the consistency of the node rankings those indices generate over ISP router-level topologies. For each pair of indices, we compute the full ranking correlation, which is the standard choice in literature, and the percentage overlap between the k top nodes. Then, we let these rankings guide the removal of highly central nodes and assess the impact on both the connectivity properties and traffic-carrying capacity of the network. Our results confirm that the top-k overlap predicts the comparative impact of indices on the network vulnerability better than the full-ranking correlation. Importantly, the locally computed degree centrality index approximates closely the global indices with the most dramatic impact on the traffic-carrying capacity; whereas, its approximative power in terms of connectivity is more topology-dependent.