Improving Classification Quality in Uncertain Graphs

Michele Dallachiesa, C. Aggarwal, Themis Palpanas
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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, then the classification accuracy in the underlying network may be affected adversely. In this article, 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.
改进不确定图的分类质量
在许多使用和分析网络数据的实际应用程序中,网络图中的链接可能是错误的或来自概率技术。在这种情况下,节点分类问题可能具有挑战性,因为链接的不可靠性可能会影响分类过程的最终结果。如果没有明确地使用链路可靠性信息,则可能会对底层网络中的分类精度产生不利影响。在本文中,我们关注需要分析图结构中存在的不确定性的情况。将不确定性视为一类公民,研究了不确定图中节点分类的新问题。我们提出了两种基于贝叶斯模型和自动参数选择的技术,并表明将不确定性纳入到一等公民的分类过程中是有益的。我们使用不同的真实数据集对所提出的方法进行了实验评估,并研究了算法在不同条件下的行为。结果表明了该方法的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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