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Faster Clustering via Non-Backtracking Random Walks
[article]
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
arXiv:1708.07967v1
fatcat:rkylk3wbujcgdkqfwp7ilsseni