Fast, memory efficient low-rank approximation of SimRank
I.V. Oseledets, G.V. Ovchinnikov
TL;DR
This paper introduces a novel low-rank approximation method for SimRank, a popular graph vertex similarity measure, aiming to improve computational efficiency and memory usage.
Contribution
The paper presents a new low-rank approximation technique for SimRank, enhancing efficiency over existing methods.
Findings
Reduced memory consumption in SimRank computations
Faster similarity calculations for large graphs
Maintained accuracy with low-rank approximation
Abstract
SimRank is a well-known similarity measure between graph vertices. In this paper novel low-rank approximation of SimRank is proposed.
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Stochastic Gradient Optimization Techniques · Tensor decomposition and applications