A regularization framework in polar coordinates for transductive learning in networked data

In networked data, linked objects tend to belong to the same class, and densely linked subgraphs are often available. Based on these facts, this paper presents a regularization framework that consists of fitting and regularization terms for transductive learning in networked data. The desirable value of the fitting term is related to the number of labeled data, whereas that of the regularization term is dependent on the structure of the graph. The ratio of these two desirable values is essential for the estimation of the optimal regularization parameters, such as that proposed in our paper. Under the proposed regularization framework, an effective classification algorithm is developed. Two methods are also introduced to incorporate contents of objects into the proposed framework to ultimately improve classification accuracy. Promising experimental results are reported on a toy problem and a paper classification task.