Abstract
In this paper, we discuss pattern mining problems from a single graph whose vertices or edges contain a set of numerical attributes. Several networks can be naturally represented in this kind of complex graph. A typical example is a social network whose vertex corresponds to a person with some numerical attributes such as age, salary and so on. Another example is a communication network whose edge represents a communication between devices. We can associate the numbers of communications per certain time period to each edge. For these kinds of complex graphs, it is meaningful to consider not only graph strucuture but also the internal informations. Although it can be expected that these kinds of data will increase rapidly, most of current graph mining algorithms do not handle these complex graphs directly. Motivated by the above background, we developed algorithms named FAGV-gSpan and FAGE-gSpan for finding frequent patterns from a single graph with numerical attributes, by effectively combining techniques of graph mining and quantitative itemset mining. The effectiveness of the proposed algorithms was confirmed by experiments using real world datasets.
