Faster Clustering via Non-Backtracking Random Walks

Brian Rappaport; Anuththari Gamage; Shuchin Aeron

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arXiv · 1708.07967

Faster Clustering via Non-Backtracking Random Walks

Brian Rappaport·Anuththari Gamage·Shuchin Aeron

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Abstract

This paper presents VEC-NBT, a variation on the unsupervised graph clustering technique VEC, which improves upon the performance of the original algorithm significantly for sparse graphs. VEC employs a novel application of the state-of-the-art word2vec model to embed a graph in Euclidean space via random walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm to use a non-backtracking random walk instead of the normal backtracking random walk used in VEC. We introduce a modification to a non-backtracking random walk, which we call a begrudgingly-backtracking random walk, and show empirically that using this model of random walks for VEC-NBT requires shorter walks on the graph to obtain results with comparable or greater accuracy than VEC, especially for sparser graphs.

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Brian Rappaport, Anuththari Gamage, Shuchin Aeron. 2017-08-26. Faster Clustering via Non-Backtracking Random Walks. https://arxiv.org/abs/1708.07967

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