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. 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 show the benefits of incorporating uncertainty in the classification process as a first-class citizen. The experimental results demonstrate the effectiveness of our approaches.

Node classification in uncertain graphs

Dallachiesa, Michele;Palpanas, Themistoklis
2014-01-01

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. 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 show the benefits of incorporating uncertainty in the classification process as a first-class citizen. The experimental results demonstrate the effectiveness of our approaches.
2014
Proceedings of the 26th International Conference on Scientific and Statistical Database Management - SSDBM '14
AA. VV.
New York
ACM
9781450327220
Dallachiesa, Michele; C., Aggarwal; Palpanas, Themistoklis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/101710
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