Directed graph learning via high-order co-linkage analysis

Abstract

Many real world applications can be naturally formulated as a directed graph learning problem. How to extract the directed link structures of a graph and use labeled vertices are the key issues to infer labels of the remaining unlabeled vertices. However, directed graph learning is not well studied in data mining and machine learning areas. In this paper, we propose a novel Co-linkage Analysis (CA) method to process directed graphs in an undirected way with the directional information preserved. On the induced undirected graph, we use a Green's function approach to solve the semi-supervised learning problem. We present a new zero-mode free Laplacian which is invertible. This leads to an Improved Green's Function (IGF) method to solve the classification problem, which is also extended to deal with multi-label classification problems. Promising results in extensive experimental evaluations on real data sets have demonstrated the effectiveness of our approach. © 2010 Springer-Verlag Berlin Heidelberg.