Mining credible attribute rules in dynamic attributed graphs

Abstract

Graph mining has numerous real-world applications. The goal is to extract interesting subgraphs or patterns in graph databases. Dynamic attributed graphs are more complex databases in which graphs change over time, and each vertex has multiple attributes. However, most algorithms only pay attention to finding rules that show relationships between nodes, and they typically have substantial limitations, such as finding rules with a single attribute or vertex and edges that do not change over time. To address these limitations, we propose an algorithm called CAR-Miner, which aims to mine credible attribute rules in dynamic attributed graphs. The algorithm incorporates a novel objective interestingness measure that is stable, anti-monotonic, and can eliminate cross-support core patterns. We conducted several experiments on real-life databases. The results show that our algorithm outperforms the state-of-the-art algorithm, especially as the number of attributes increases. Additionally, we find patterns that have practical significance and can be interpreted in various ways. Overall, CAR-Miner is a promising algorithm for mining patterns in dynamic attributed graphs, which can help researchers and practitioners identify valuable patterns that were previously difficult to discover.