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Recently, graphs have been widely used to represent many different kinds ofreal world data or observations such as social networks, protein-proteinnetworks, road networks, and so on. In many cases, each node in a graph isassociated with a set of its attributes and it is critical to not only considerthe link structure of a graph but also use the attribute information to achievemore meaningful results in various graph mining tasks. Most previous works withattributed graphs take into ac- count attribute relationships only betweenindividually connected nodes. However, it should be greatly valuable to findout which sets of attributes are associated with each other and whether theyare statistically significant or not. Mining such significant associations, wecan uncover novel relationships among the sets of attributes in the graph. Wepropose an algorithm that can find those attribute associations efficiently andeffectively, and show experimental results that confirm the high applicabilityof the proposed algorithm.

Mining Statistically Significant Attribute Associations in Attributed Graphs