Semi-supervised Learning on Directed Graphs

Given a directed graph in which some of the nodes are labeled, we inves- tigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions de(cid: 2)ned over nodes of a directed graph that forces the classi(cid: 2)cation function to change slowly on densely linked subgraphs. A powerful, yet computationally simple classi(cid: 2)cation algorithm is derived within the proposed framework. The experimental evaluation on real-world Web classi(cid: 2)cation problems demonstrates en- couraging results that validate our approach.

Authors

• Dengyong Zhou
• Thomas Hofmann ETH Zürich
• Bernhard Schölkopf

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