Transductive Classification Methods for Mixed Graphs

Sundararajan Sellamanickam; Sathiya Keerthi Selvaraj

Transductive Classification Methods for Mixed Graphs

Machine Learning 2012-06-27 v1 Machine Learning

Authors: Sundararajan Sellamanickam , Sathiya Keerthi Selvaraj

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.

Keywords

graph representation learning graph theory graph generation

Cite

@article{arxiv.1206.6015,
  title  = {Transductive Classification Methods for Mixed Graphs},
  author = {Sundararajan Sellamanickam and Sathiya Keerthi Selvaraj},
  journal= {arXiv preprint arXiv:1206.6015},
  year   = {2012}
}

Comments

8 Pages, 2 Tables, 2 Figures, KDD Workshop - MLG'11 San Diego, CA, USA

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