Power-of-Metric Embedding for Network Dissimilarity Estimation

The geometric embedding approach to network dissimilarity estimation assumes a linear relationship between the dissimilarity and the intrinsic metric. In this paper, we conjecture that this relationship should better be formulated by a power law. We propose a geometric framework based on this conjecture and show that by choosing the right power coefficient one can achieve better estimation accuracy. This is substantiated in an evaluation study using both synthetic and real-world datasets.