A fast algorithm for mining temporal association rules in a multi-attributed graph sequence

In real life, there exist a lot of attributed graphs each of which contains attribute information as well as structural information. As time goes on, a group of attributed graphs form an attributed graph sequence. Being the generalization of single-attributed graph sequences, multi-attributed graph sequences are arising vastly and quickly. Mining the temporal associations hidden in a multi-attributed graph sequence is in urgent need from data owners. To meet the need and fill the gap of research on mining such kind of temporal associations, we first give a definition of temporal association rules for describing temporal associations in a multi-attributed graph sequence, and then propose a fast algorithm for mining temporal association rules in a multi-attributed graph sequence which is based on the anti-monotonicity of support. The proposed algorithm is designed in two steps, namely finding frequent temporal association rules and verifying the credibility of these rules. Equipped with two novel joining and pruning strategies, the proposed algorithm exhibits much higher efficiency which is specially pursued in the process of rule mining. Experiments performed on synthetic datasets and real datasets show that the proposed algorithm is effective and more efficient than other existing algorithms.

Automated summary

The study proposes a fast algorithm based on the anti-monotonicity of support for mining temporal associations in multi-attributed graph sequences. Experiments show that the algorithm is more effective and efficient in terms of efficiency and accuracy compared to other existing algorithms.