Node Classification in Uncertain Graphs

TL;DR

This paper addresses node classification in uncertain graphs by explicitly modeling link unreliability, proposing Bayesian-based techniques, and demonstrating improved accuracy through experiments on real datasets.

Contribution

It introduces a novel approach that treats uncertainty as a first-class citizen in node classification, with two Bayesian-based methods and automatic parameter selection.

Findings

Incorporating uncertainty improves classification accuracy.

The proposed methods are effective and efficient on real datasets.

Explicit modeling of link unreliability benefits network analysis.

Abstract

In many real applications that use and analyze networked data, the links in the network graph may be erroneous, or derived from probabilistic techniques. In such cases, the node classification problem can be challenging, since the unreliability of the links may affect the final results of the classification process. If the information about link reliability is not used explicitly, the classification accuracy in the underlying network may be affected adversely. In this paper, we focus on situations that require the analysis of the uncertainty that is present in the graph structure. We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen. We propose two techniques based on a Bayes model and automatic parameter selection, and show that the incorporation of uncertainty in the classification process as a first-class citizen is beneficial. We experimentally evaluate the proposed approach using different real data sets, and study the behavior of the algorithms under different conditions. The results demonstrate the effectiveness and efficiency of our approach.