Transductive Classification Methods for Mixed Graphs
This addresses a limitation in graph-based classification methods for scenarios where relationships can be both similar and dissimilar, though it is incremental as it builds on existing techniques.
The paper tackles the problem of transductive classification on mixed graphs, which include both similar and dissimilar edges, by extending existing methods like Information Regularization and Weighted Vote Relational Neighbor classifier to handle such graphs, and demonstrates their usefulness on benchmark and real-world datasets.
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.