K-Nearest Neighbor Graph (K-NNG) construction is an important operation with many web related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Existing methods for K-NNG construction either do not scale, or are specific to certain similarity measures. We present NN-Descent, a simple yet efficient algorithm for approximate K-NNG construction with arbitrary similarity measures. Our method is based on local search, has minimal space overhead and does not rely on any shared global index. Hence, it is especially suitable for large-scale applications where data structures need to be distributed over the network. We have shown with a variety of datasets and similarity measures that the proposed method typically converges to above 90% recall with each point comparing only to several percent of the whole dataset on average.

dered nanostructures could be fabricated by using a patterned substrate (for example the patterned PDMS stamps) as the other substrate in the formation of colloidal crystals.AFM images were recorded in the contact mode with a Nanoscope IIIa scanning probe microscope from Digital Instruments under ambient conditions. SEM micrographs were taken with a JEOL FESEM 6700F electron microscope with primary electron energy of 3 kV. The samples with gold films on the polymer films were not sputtered prior to imaging.

Supplementary data are available at Bioinformatics online.

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