HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks-Reference-Cited by-同舟云学术

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HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

Published:2020-01 Issue: Volume: Page:460-468
ISSN:
Container-title:Proceedings of the 2020 SIAM International Conference on Data Mining
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Author:

LaRock Timothy,Nanumyan Vahan,Scholtes Ingo,Casiraghi Giona,Eliassi-Rad Tina,Schweitzer Frank

Publisher

Society for Industrial and Applied Mathematics

Link

https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.52

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Reconstructing signed relations from interaction data;Scientific Reports;2023-11-24

2. Locating community smells in software development processes using higher-order network centralities;Social Network Analysis and Mining;2023-10-10

3. Predicting variable-length paths in networked systems using multi-order generative models;Applied Network Science;2023-09-22

4. caSPiTa: mining statistically significant paths in time series data from an unknown network;Knowledge and Information Systems;2023-02-02

5. Predicting Influential Higher-Order Patterns in Temporal Network Data;2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM);2022-11-10

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