Mining Statistically Significant Attribute Associations in Attributed Graphs

TL;DR

This paper introduces an efficient algorithm to discover statistically significant attribute associations in attributed graphs, enhancing the understanding of complex relationships beyond direct node connections.

Contribution

The paper presents a novel algorithm for mining significant attribute associations in attributed graphs, addressing the gap of considering attribute sets rather than individual node relationships.

Findings

Algorithm effectively finds significant attribute associations

Experimental results confirm high applicability and efficiency

Uncovers novel attribute relationships in various graph types

Abstract

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.