Abstract
Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. Prior subgraph discovery problem over streaming graphs mostly focuses on characteristics like frequency and burstiness. Persistence, as a new characteristic, is getting increasing attention. Persistent subgraph discovery highlights behaviors where a subgraph appears recurrently in many time windows, which is vital for many real-world applications (e.g., anomaly detection). While persistent subgraph discovery enjoys many interesting real-life applications, there is no off-the-shelf solution to compute the persistent pattern efficiently. In this work, we are the first to study the persistent subgraph pattern discovering problem over the streaming graph. We devise an auxiliary data structure called \(\textsf {TFD} \) to detect the persistent subgraph patterns in real-time with limited memory usage. \(\textsf {TFD} \) maps each subgraph into the corresponding bucket based on hash functions to compute the persistence of each pattern. Then we introduce optimizations to separate persistent and non-persistent patterns, further improving the effectiveness and throughput in space-scarce scenarios. Extensive experiments confirm the superiority of our proposed method.
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Acknowledgements
This work is partially supported by National Natural Science Foundation of China under Grant No. U19B2024, National Natural Science Foundation of China under Grant No.6227246 and Postgraduate Scientific Research Innovation Project of Hunan Province under Grant No. CX20210038.
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Huang, C., Zhang, Q., Guo, D., Zhao, X. (2023). Discovering Persistent Subgraph Patterns over Streaming Graphs. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13945. Springer, Cham. https://doi.org/10.1007/978-3-031-30675-4_11
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DOI: https://doi.org/10.1007/978-3-031-30675-4_11
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