Principal pattern mining on graphs
Given a graph, can we find a set of patterns, of which the cost of storing these patterns is economic (or satisfying specific user needs) but their coverage includes the entire graph? We denote these patterns by principal patterns of the given graph since they can be regarded as its composition elements, which can be a signature for summarizing a graph of various sizes. Note that different principal patterns can contribute different sizes of graph coverage so they are not necessarily the frequent patterns. In this paper, we show that the recursive method can obtain the optimal solution while the greedy algorithms can find approximations with lower time complexity. Furthermore, we propose an effective pruning method that can be combined with both algorithms such that the mining process is even more efficient and scalable. Experiment results show that the proposed algorithms can efficiently and effectively discover the principal patterns.
