Faster Clustering via Non-Backtracking Random Walks [article]

Brian Rappaport, Anuththari Gamage, Shuchin Aeron
2017 arXiv   pre-print
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
more » ... ification 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.
arXiv:1708.07967v1 fatcat:rkylk3wbujcgdkqfwp7ilsseni