Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
Summary: AnoT detects online anomalies in temporal knowledge graphs via a rule-graph summary, enabling interpretable inference. D-U-M pipeline supports offline summarization, online scoring, rule updates, and error estimation, yielding interpretable signals. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiasheng Zhang (University of Electronic Science and Technology of China)
- 2. Rex Ying (Yale University)
- 3. Jie Shao (Shenzhen University; University of Electronic Science and Technology of China)
BibTeX Citation
@inproceedings{zhang_sigmod24,
title = {{Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability}},
author = {Zhang, Jiasheng and Ying, Rex and Shao, Jie},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3698823},
url = {https://dl.acm.org/doi/10.1145/3698823},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,835 | Sankofa: Online Query-adaptive Dynamic Graph Summaries | 2026 | VLDB | 4.9793485e-05 |
| 10,939 | BiLink: Bidirectional Meta-paths for Link Discovery in Billion-Scale Heterogeneous Graphs | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,873 | Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs | 2014 | VLDB | 9.4579851e-05 |
| 3,154 | Managing Large Dynamic Graphs Efficiently | 2012 | SIGMOD | 7.5870734e-05 |
| 4,647 | Mining Graph Patterns Efficiently via Randomized Summaries | 2009 | VLDB | 6.4867875e-05 |
| 5,331 | Localizing Anomalous Changes in Time-evolving Graphs | 2014 | SIGMOD | 6.176792e-05 |
| 5,379 | Efficient Knowledge Graph Accuracy Evaluation | 2019 | VLDB | 6.1558829e-05 |
| 5,764 | Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition | 2018 | VLDB | 6.0030951e-05 |
| 5,924 | Summarizing Answer Graphs Induced by Keyword Queries | 2013 | VLDB | 5.9458126e-05 |
| 7,369 | Kelpie: an Explainability Framework for Embedding-based Link Prediction Models | 2022 | VLDB | 5.5407601e-05 |
| 7,549 | Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph | 2023 | VLDB | 5.4981129e-05 |
| 8,218 | Towards Event Prediction in Temporal Graphs | 2022 | VLDB | 5.3762638e-05 |
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| 1 | 8,401 | Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data | 2007 | VLDB |
| 2 | 5,331 | Localizing Anomalous Changes in Time-evolving Graphs | 2014 | SIGMOD |
| 3 | 7,645 | Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion | 2025 | VLDB |
| 4 | 12,368 | Graph-based Exploration of Non-graph Datasets | 2016 | VLDB |
| 5 | 1,005 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB |
| 6 | 6,268 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB |
| 7 | 2,375 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB |
| 8 | 6,197 | GraphAn: Graph-based Subsequence Anomaly Detection | 2020 | VLDB |
| 9 | 3,449 | Robust and Transferable Log-based Anomaly Detection | 2023 | SIGMOD |
| 10 | 7,549 | Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph | 2023 | VLDB |