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Efficient Computation Of Mean Truncated Hitting Times On Very Large Graphs

Joel Lang, James Henderson . Arxiv 2013 – 0 citations

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Datasets Efficiency Graph Based ANN Recommender Systems Scalability

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.

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