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Improved Landmark-based Shortest Path Length Estimation in Large Graphs with Distance Correction

The existence of massive network graphs with hundreds of millions of nodes that support many online services such as messaging systems, social networks, routing infrastructures, among others, pose unique challenges to analysis algorithms. Many applications rely on computing distances between node pairs. Computing the shortest distance between a pair of nodes is an essential operation that can take a long time in large graphs using traditional algorithms. In this paper, we study approximate landmark-based methods for shortest path distance that scale to large graphs. These methods rely on selecting a set of landmark nodes used to estimate the distance at running time quickly. Our work offers two main results: we combine existing metrics to select landmarks that offer limited performance improvements but work well in some graphs, and we propose distance correction methods that boost the performance of the previous ones significantly. These methods are new in the literature for this class of distance estimators.