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
No non-self incoming citations found for this paper in this database.
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 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 1,944 | Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs | 2014 | VLDB | 9.4354517e-05 |
| 3,109 | Managing Large Dynamic Graphs Efficiently | 2012 | SIGMOD | 7.7482727e-05 |
| 4,581 | Mining Graph Patterns Efficiently via Randomized Summaries | 2009 | VLDB | 6.6198548e-05 |
| 5,236 | Localizing Anomalous Changes in Time-evolving Graphs | 2014 | SIGMOD | 6.3044413e-05 |
| 5,671 | Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition | 2018 | VLDB | 6.1262839e-05 |
| 5,849 | Summarizing Answer Graphs Induced by Keyword Queries | 2013 | VLDB | 6.0666529e-05 |
| 6,581 | Efficient Knowledge Graph Accuracy Evaluation | 2019 | VLDB | 5.8364579e-05 |
| 7,413 | Kelpie: an Explainability Framework for Embedding-based Link Prediction Models | 2022 | VLDB | 5.624223e-05 |
| 7,457 | Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph | 2023 | VLDB | 5.6124322e-05 |
| 8,095 | Towards Event Prediction in Temporal Graphs | 2022 | VLDB | 5.4877864e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,229 | Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data | 2007 | VLDB |
| 2 | 5,236 | Localizing Anomalous Changes in Time-evolving Graphs | 2014 | SIGMOD |
| 3 | 10,935 | Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion | 2025 | VLDB |
| 4 | 12,075 | Graph-based Exploration of Non-graph Datasets | 2016 | VLDB |
| 5 | 1,029 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB |
| 6 | 6,141 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB |
| 7 | 2,346 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB |
| 8 | 6,109 | GraphAn: Graph-based Subsequence Anomaly Detection | 2020 | VLDB |
| 9 | 3,376 | Robust and Transferable Log-based Anomaly Detection | 2023 | SIGMOD |
| 10 | 7,457 | Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph | 2023 | VLDB |