Measuring the effectiveness of the s-metric to produce better network models

Recent research has shown that while many complex networks follow a power-law-distribution for their node degrees, it is not sufficient to model these networks based only on their degree distribution. In order to better distinguish between these networks, the metric s was introduced to measure how interconnected the hub nodes are in a network. We examine the effectiveness of creating network models based on this metric. Through a series of computational experiments, we compare how well a set of common structural network metrics are preserved between instances of the autonomous system Internet topology and a series of random models with identical degree sequences and similar s values. We demonstrate that creating models based on the s metric can produce moderate improvement in structural characteristics over strictly using degree distribution. Our results also indicate that some interesting relationships exist between the s metric and the various structural metrics.