In this paper, we focus on a single graph whose vertices contain a set of quantitative attributes. Several networks can be naturally represented in this complex graph. An example is a social network whose vertex corresponds to a person with some quantitative items such as age, salary and so on. Although it can be expected that this kind of data will increase rapidly, most of current graph mining algorithms do not handle these complex graphs directly. Motivated by the above background, by effectively combining techniques of graph mining and quantitative itemset mining, we developed an algorithm named FAG-gSpan for finding frequent patterns from a graph with quantitative itemsets.
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