In this paper, we present a general data clustering algorithm which is based on the asymmetric pairwise measure of Markov random walk hitting time on directed graphs. Unlike tradi...
Graph theoretic problems are representative of fundamental computations in traditional and emerging scientific disciplines like scientific computing, computational biology and b...
This paper presents a system for graph clustering where users can visualize the clustering and give "hints" that help a computing method to find better solutions. Hints ...
In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points' (often symmetric) relationships in input space...
We introduce a graph clustering problem motivated by a stream processing application. Input to our problem is an undirected graph with vertex and edge weights. A cluster is a subse...