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Abstract

We propose a general framework for learning from labeled and
unlabeled data on a directed graph in which the structure of the
graph including the directionality of the edges is considered. The
time complexity of the algorithm derived from this framework is
nearly linear due to recently developed numerical techniques. In
the absence of labeled instances, this framework can be utilized
as a spectral clustering method for directed graphs, which
generalizes the spectral clustering approach for undirected
graphs. We have applied our framework to real-world web
classification problems and obtained encouraging results.