Mining attribute evolution rules in dynamic attributed graphs
Fournier-Viger, P., He, G., Lin, J. C.-W., & Gomes, H. M. (2020). Mining attribute evolution rules in dynamic attributed graphs. In M. Song, I.-Y. Song, G. Kotsis, A. M. Tjoa, & I. Khalil (Eds.), Proceeding of 22nd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2020) (Vol. LNCS 12393, pp. 167–182). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-59065-9_14
Permanent Research Commons link: https://hdl.handle.net/10289/14107
A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies have been done on discovering patterns in dynamic attributed graphs to reveal how attribute(s) change over time. However, many algorithms restrict all attribute values in a pattern to follow the same trend (e.g. increase) and the set of vertices in a pattern to be fixed, while others consider that a single vertex may influence its neighbors. As a result, these algorithms are unable to find complex patterns that show the influence of multiple vertices on many other vertices in terms of several attributes and different trends. This paper addresses this issue by proposing to discover a novel type of patterns called attribute evolution rules (AER). These rules indicate how changes of attribute values of multiple vertices may influence those of others with a high confidence. An efficient algorithm named AER-Miner is proposed to find these rules. Experiments on real data show AER-Miner is efficient and that AERs can provide interesting insights about dynamic attributed graphs.
This is a post-peer-review, pre-copyedit version of an article published in Big Data Analytics and Knowledge Discovery. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-59065-9_14”