  Recently, graphs have been widely used to represent many different kinds of
real world data or observations such as social networks, protein-protein
networks, road networks, and so on. In many cases, each node in a graph is
associated with a set of its attributes and it is critical to not only consider
the link structure of a graph but also use the attribute information to achieve
more meaningful results in various graph mining tasks. Most previous works with
attributed graphs take into ac- count attribute relationships only between
individually connected nodes. However, it should be greatly valuable to find
out which sets of attributes are associated with each other and whether they
are statistically significant or not. Mining such significant associations, we
can uncover novel relationships among the sets of attributes in the graph. We
propose an algorithm that can find those attribute associations efficiently and
effectively, and show experimental results that confirm the high applicability
of the proposed algorithm.
