Efficient Computation of Mean Truncated Hitting Times on Very Large Graphs

TL;DR

This paper introduces a new approximation algorithm that enables the efficient computation of mean truncated hitting times on very large graphs, expanding their applicability in large-scale graph-based learning tasks.

Contribution

The paper presents a novel approximation method that allows for scalable computation of hitting times on large, disk-resident graphs, overcoming previous computational limitations.

Findings

Enables computation of hitting times on large graphs

Improves scalability of graph-based dissimilarity measures

Facilitates application in large-scale learning problems

Abstract

Previous work has shown the effectiveness of random walk hitting times as a measure of dissimilarity in a variety of graph-based learning problems such as collaborative filtering, query suggestion or finding paraphrases. However, application of hitting times has been limited to small datasets because of computational restrictions. This paper develops a new approximation algorithm with which hitting times can be computed on very large, disk-resident graphs, making their application possible to problems which were previously out of reach. This will potentially benefit a range of large-scale problems.