  In this paper we provide a principled approach to solve a transductive
classification problem involving a similar graph (edges tend to connect nodes
with same labels) and a dissimilar graph (edges tend to connect nodes with
opposing labels). Most of the existing methods, e.g., Information
Regularization (IR), Weighted vote Relational Neighbor classifier (WvRN) etc,
assume that the given graph is only a similar graph. We extend the IR and WvRN
methods to deal with mixed graphs. We evaluate the proposed extensions on
several benchmark datasets as well as two real world datasets and demonstrate
the usefulness of our ideas.
