A Graph Clustering Approach to Computing Network Coordinates

In the technique known as network coordinates, the network latency between nodes is modeled as the distance between points in a metric space. Actual network latencies, however, exhibit numerous triangle inequality violations, which result in significant error between the actual latency and the distance as determined by the network coordinates. In this work, we show how graph clustering techniques can be used to find regions of the network space that show low triangle inequality violation within the region. By using techniques to increase the relative edge density in these regions, we improve the accuracy of network coordinates in these regions. We reduce the relative error within a cluster by 15% on average for the Meridian dataset, and by 7% over all; when compared to a single spring relaxation over the whole network.