document: Fast direction-aware proximity for graph mining In this paper we study asymmetric proximity measures on directed graphs , which quantify the relationships between two nodes or two groups of nodes . The measures are useful in several graph mining tasks , including clustering , link prediction and connection subgraph discovery . Our proximity measure is based on the conceptof escape probability . This way , we strive to summarize the multiple facets of nodes-proximity , while avoiding some of the pitfalls to which alternative proximity measures are susceptible . A unique feature of the measures is accounting for the underlying directional information . We put a special emphasis on computational efficiency , and develop fast solutions that are applicable in several settings . Our experimental study shows the usefulness of our proposed direction-aware proximity method for several applications , and that our algorithms achieve a significant speedup ( up to 50,000 x ) over straight forward implementations .
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