Transductive Classification Methods for Mixed Graphs

TL;DR

This paper introduces extended transductive classification methods for mixed graphs containing both similar and dissimilar edges, improving upon existing methods that only handle similar graphs, and demonstrates their effectiveness on various datasets.

Contribution

The paper extends existing transductive classification methods to effectively handle mixed graphs with both similar and dissimilar edges, a scenario not addressed by prior approaches.

Findings

Extended IR and WvRN methods perform well on benchmark datasets.

Proposed methods outperform traditional similar-graph-only approaches.

Effective on real-world datasets with mixed graph structures.

Abstract

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.