Benchmark for Discriminating Power of Edge Centrality Metrics

Edge centrality has found wide applications in various aspects. Many edge centrality metrics have been proposed, but the crucial issue that how good the discriminating power of a metric is, with respect to other measures, is still open. In this paper, we address the question about the benchmark of the discriminating power of edge centrality metrics. We first use the automorphism concept to define equivalent edges, based on which we introduce a benchmark for the discriminating power of edge centrality measures and develop a fast approach to compare the discriminating power of different measures. According to the benchmark, for a desirable measure, equivalent edges have identical metric scores, while inequivalent edges possess different scores. However, we show that even in a toy graph, inequivalent edges cannot be discriminated by three existing edge centrality metrics. We then present a novel edge centrality metric called forest centrality. Extensive experiments on real-world networks and model networks indicate that forest centrality has better discriminating power than three existing edge centrality metrics.

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Recommended citation: Bao Qi, Xu Wanyue, and Zhang Zhongzhi. Benchmark for discriminating power of edge centrality metrics. The Computer Journal, 2021.