Discovering Inter-Dimensional Rules in Dynamic Graphs

Data mining methods that exploit graph/network have become quite popular and a timely challenge is to consider the discovery of dynamic properties in evolving graphs or networks. In this paper, we consider the dynamic oriented graphs that can be encoded as n-ary relations with n ≥ 3 such that we have a least 3 dimensions: the dimensions of departure (tail) and arrival (head) vertices plus the time dimension. In other terms, it encodes the sequence of adjacency matrices of the graph. In such datasets, we propose a new semantics for inter-dimensional rules in dynamic graphs. We define rules that may involve subsets of any dimensions in their antecedents and their consequents and we propose the new objective interestingness measure called the exclusive confidence. We introduce a first algorithm for computing such inter-dimensional rules and we illustrate the added-value of exclusive confidence for supporting the discovery of relevant rules from a real-life dynamic graph.