Active Betweenness Cardinality: Algorithms and Applications

TL;DR

This paper introduces active betweenness cardinality, a new network centrality measure based on activity rather than topology, enabling identification of critical nodes in large, distributed, and dynamic networks.

Contribution

It proposes a novel activity-based centrality measure, algorithms for its efficient computation using local information, and applications for network monitoring and failure detection.

Findings

Effective in identifying failed nodes through local measurements

Can be computed efficiently with limited information

Useful for monitoring large, distributed networks

Abstract

Centrality rankings such as degree, closeness, betweenness, Katz, PageRank, etc. are commonly used to identify critical nodes in a graph. These methods are based on two assumptions that restrict their wider applicability. First, they assume the exact topology of the network is available. Secondly, they do not take into account the activity over the network and only rely on its topology. However, in many applications, the network is autonomous, vast, and distributed, and it is hard to collect the exact topology. At the same time, the underlying pairwise activity between node pairs is not uniform and node criticality strongly depends on the activity on the underlying network. In this paper, we propose active betweenness cardinality, as a new measure, where the node criticalities are based on not the static structure, but the activity of the network. We show how this metric can be computed efficiently by using only local information for a given node and how we can find the most critical nodes starting from only a few nodes. We also show how this metric can be used to monitor a network and identify failed nodes.We present experimental results to show effectiveness by demonstrating how the failed nodes can be identified by measuring active betweenness cardinality of a few nodes in the system.