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Exploiting edge semantics in citation graphs using efficient, vertical ARM

机译：使用有效的垂直ARM在引用图中利用边缘语义

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摘要

Graphs are increasingly becoming a vital source of information within which a great deal of semantics is embedded. As the size of available graphs increases, our ability to arrive at the embedded semantics grows into a much more complicated task. One form of important hidden semantics is that which is embedded in the edges of directed graphs. Citation graphs serve as a good example in this context. This paper attempts to understand temporal aspects in publication trends through citation graphs, by identifying patterns in the subject matters of scientific publications using an efficient, vertical association rule mining model. Such patterns can (a) indicate subject-matter evolutionary history, (b) highlight subject-matter future extensions, and (c) give insights on the potential effects of current research on future research. We highlight our major differences with previous work in the areas of graph mining, citation mining, and Web-structure mining, propose an efficient vertical data representation model, introduce a new subjective interestingness measure for evaluating patterns with a special focus on those patterns that signify strong associations between properties of cited papers and citing papers, and present an efficient algorithm for the purpose of discovering rules of interest followed by a detailed experimental analysis.
机译：图形越来越成为一种重要的信息源，其中嵌入了大量语义。随着可用图的大小增加，我们达到嵌入式语义的能力变得更加复杂。重要的隐藏语义的一种形式是嵌入有向图的边缘的形式。在这种情况下，引文图就是一个很好的例子。本文试图通过使用有效的垂直关联规则挖掘模型来识别科学出版物主题中的模式，从而通过引用图来理解出版物趋势中的时间方面。这种模式可以（a）指明主题的演变历史，（b）突出主题的未来扩展，以及（c）洞悉当前研究对未来研究的潜在影响。我们重点介绍了与以前在图形挖掘，引文挖掘和Web结构挖掘领域的工作之间的主要差异，提出了有效的垂直数据表示模型，引入了一种新的主​​观兴趣度评估模型的评估方法，特别关注那些表示引用论文和被引用论文的属性之间的强关联，并提出了一种有效的算法，目的是发现感兴趣的规则，然后进行详细的实验分析。

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