Using the Mutual k-Nearest Neighbor Graphs for Semi-supervised Classification on Natural Language Data

The first step in graph-based semi-supervised classification is to construct a graph from input data. While the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper advocates using the less well-known mutual k-nearest neighbor graphs for high-dimensional natural language data. To compare the performance of these two graph construction methods, we run semi-supervised classification methods on both graphs in word sense disambiguation and document classification tasks. The experimental results show that the mutual k-nearest neighbor graphs, if combined with maximum spanning trees, consistently outperform the knearest neighbor graphs. We attribute better performance of the mutual k-nearest neighbor graph to its being more resistive to making hub vertices. The mutual k-nearest neighbor graphs also perform equally well or even better in comparison to the state-of-the-art b-matching graph construction, despite their lower computational complexity.