Abstract
Discovering patterns in dynamic attributed graphs allows to capture how attribute values and graph structures changes over time. This allows to understand how a graph has evolved and may change in the future, to support decision-making. But an important limitation of current studies is that they mainly select patterns based on their frequency. Thus, they may find many frequent but weakly correlated patterns. To discover strongly correlated patterns, this paper proposes a novel significance measure named Sequence Virtual Growth Rate. It allows evaluating if a pattern represents entities that are correlated in terms of their proximity in a graph over time. Based on this measure a novel type of graph patterns is defined called Significant Trend Sequence. To efficiently mine these patterns, an algorithm named TSeqMiner is proposed, which relies on a novel upper bound and pruning strategy to reduce the search space. Experiments show that the algorithm is efficient and can identify interesting patterns in social network and spatio-temporal data.
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Fournier-Viger, P., Cheng, C., Cheng, Z., Lin, J.CW., Selmaoui-Folcher, N. (2019). Finding Strongly Correlated Trends in Dynamic Attributed Graphs. In: Ordonez, C., Song, IY., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2019. Lecture Notes in Computer Science(), vol 11708. Springer, Cham. https://doi.org/10.1007/978-3-030-27520-4_18
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