摘要:在确定图上进行的相似性连接已有许多研究成果.然而，在实际应用中会有许多因素使得图结构数据变得不确定.研究了不确定图数据库上的相似性连接问题.采用联合概率分布表示法来描述图中边的不确定性，结合一种新的图的相似性度量方法，给出了不确定图数据库上的相似性连接的形式化定义，并设计了一组过滤策略来减少连接过程中候选图对的数量.大量的实验数据表明，所提出的方法具有较好的可行性和准确性.



Abstract:Many studies have been conducted on similarity join over certain (deterministic) graphs. However, in reality, graphs are often uncertain due to various factors. This paper studies similarity join on uncertain graph databases. The study employs the joint probability distribution to describe the uncertainty of edges in the graph, combines a new measure to evaluate graph similarity, and gives the formal definition of the similarity join on uncertain graph database. The paper also designs a group of filtering strategies to reduce the candidate pairs in the similarity join. A large number of experimental data show that, the method proposed in the paper is feasible and accurate.



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