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Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

Authors: Jiasheng Zhang, Rex Ying, Jie ShaoAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 6

Article No.: 247, Pages 1 - 26

https://doi.org/10.1145/3698823

Published: 20 December 2024 Publication History

Abstract

Temporal knowledge graphs (TKGs) are valuable resources for capturing evolving relationships among entities, yet they are often plagued by noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches struggle to capture the rich semantics introduced by node and edge categories within TKGs, while TKG embedding methods lack interpretability, undermining the credibility of anomaly detection. Moreover, these methods falter in adapting to pattern changes and semantic drifts resulting from knowledge updates. To tackle these challenges, we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs. When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge. The traversal yields reachable nodes that furnish interpretable evidence for the validity or the anomalous of the new knowledge. Overall, AnoT embodies a detector-updater-monitor architecture, encompassing a detector for offline TKG summarization and online scoring, an updater for real-time rule graph updates based on emerging knowledge, and a monitor for estimating the approximation error of the rule graph. Experimental results on four real-world datasets demonstrate that AnoT surpasses existing methods significantly in terms of accuracy and interoperability. All of the raw datasets and the implementation of AnoT are provided in https://github.com/zjs123/ANoT.

References

[1]

Charu C. Aggarwal, Yuchen Zhao, and Philip S. Yu. 2011. Outlier detection in graph streams. In Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, April 11--16, 2011, Hannover, Germany. 399--409.

[2]

Rafaël Van Belle, Charles Van Damme, Hendrik Tytgat, and Jochen De Weerdt. 2022. Inductive Graph Representation Learning for fraud detection. Expert Syst. Appl., Vol. 193 (2022), 116463.

Digital Library

[3]

Caleb Belth, Xinyi Zheng, and Danai Koutra. 2020. Mining Persistent Activity in Continually Evolving Networks. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020. 934--944.

[4]

Caleb Belth, Xinyi Zheng, Jilles Vreeken, and Danai Koutra. 2020. What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020. 1115--1126.

Digital Library

[5]

Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, and Bryan Hooi. 2023. Sketch-Based Anomaly Detection in Streaming Graphs. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6--10, 2023. 93--104.

Digital Library

[6]

Maximilian Blasi, Manuel Freudenreich, Johannes Horvath, David Richerby, and Ansgar Scherp. 2022. Graph Summarization with Graph Neural Networks. CoRR, Vol. abs/2203.05919 (2022).

[7]

Sejla Cebiric, Franccois Goasdoué, Haridimos Kondylakis, Dimitris Kotzinos, Ioana Manolescu, Georgia Troullinou, and Mussab Zneika. 2019. Summarizing semantic graphs: a survey. VLDB J., Vol. 28, 3 (2019), 295--327.

Digital Library

[8]

Yen-Yu Chang, Pan Li, Rok Sosic, M. H. Afifi, Marco Schweighauser, and Jure Leskovec. 2021. F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams. In WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8--12, 2021. 589--597.

[9]

Chen Chen, Cindy Xide Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan, and Jiawei Han. 2009. Mining Graph Patterns Efficiently via Randomized Summaries. Proc. VLDB Endow., Vol. 2, 1 (2009), 742--753.

Digital Library

[10]

Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha P. Talukdar. 2018. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018. 2001--2011.

[11]

Kaize Ding, Jundong Li, Nitin Agarwal, and Huan Liu. 2020. Inductive Anomaly Detection on Attributed Networks. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020. 1288--1294.

[12]

Dhivya Eswaran and Christos Faloutsos. 2018. SedanSpot: Detecting Anomalies in Edge Streams. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17--20, 2018. 953--958.

[13]

Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra. 2018. SpotLight: Detecting Anomalies in Streaming Graphs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2018. 1378--1386.

Digital Library

[14]

Wenfei Fan, Ruochun Jin, Ping Lu, Chao Tian, and Ruiqi Xu. 2022. Towards Event Prediction in Temporal Graphs. Proc. VLDB Endow., Vol. 15, 9 (2022), 1861--1874.

Digital Library

[15]

Yujie Fan, Yanfang Ye, Qian Peng, Jianfei Zhang, Yiming Zhang, Xusheng Xiao, Chuan Shi, Qi Xiong, Fudong Shao, and Liang Zhao. 2020. Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. In 20th IEEE International Conference on Data Mining, ICDM 2020, Sorrento, Italy, November 17--20, 2020. 132--141.

[16]

Lanting Fang, Kaiyu Feng, Jie Gui, Shanshan Feng, and Aiqun Hu. 2023. Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graphs. Proc. VLDB Endow., Vol. 16, 5 (2023), 1154--1167.

Digital Library

[17]

Esther Galbrun. 2022. The minimum description length principle for pattern mining: a survey. Data Min. Knowl. Discov., Vol. 36, 5 (2022), 1679--1727.

Digital Library

[18]

Junyang Gao, Xian Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong, and Jun Yang. 2019. Efficient Knowledge Graph Accuracy Evaluation. Proc. VLDB Endow., Vol. 12, 11 (2019), 1679--1691.

Digital Library

[19]

Alberto García-Durán, Sebastijan Dumancic, and Mathias Niepert. 2018. Learning Sequence Encoders for Temporal Knowledge Graph Completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018. 4816--4821.

[20]

Rishab Goel, Seyed Mehran Kazemi, Marcus A. Brubaker, and Pascal Poupart. 2020. Diachronic Embedding for Temporal Knowledge Graph Completion. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, New York, NY, USA, February 7--12, 2020. 3988--3995.

[21]

Xingzhi Guo, Baojian Zhou, and Steven Skiena. 2022. Subset Node Anomaly Tracking over Large Dynamic Graphs. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. 475--485.

Digital Library

[22]

Pankaj Gupta, Venu Satuluri, Ajeet Grewal, Siva Gurumurthy, Volodymyr Zhabiuk, Quannan Li, and Jimmy Lin. 2014. Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs. Proc. VLDB Endow., Vol. 7, 13 (2014), 1379--1380.

Digital Library

[23]

Mahdi Hajiabadi, Venkatesh Srinivasan, and Alex Thomo. 2022. Dynamic Graph Summarization: Optimal and Scalable. In IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, December 17--20, 2022. 545--554.

[24]

David A. Huffman. 1952. A Method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE., Vol. 40, 9 (1952), 1098--1101.

[25]

Prachi Jain, Sushant Rathi, Mausam, and Soumen Chakrabarti. 2020. Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16--20, 2020. 3733--3747.

[26]

Shengbin Jia, Yang Xiang, Xiaojun Chen, Kun Wang, and Shijia E. 2019. Triple Trustworthiness Measurement for Knowledge Graph. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13--17, 2019. 2865--2871.

[27]

Jaehun Jung, Jinhong Jung, and U Kang. 2021. Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14--18, 2021. 786--795.

Digital Library

[28]

Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020. Tensor Decompositions for Temporal Knowledge Base Completion. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020.

[29]

Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. 2021. Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021. 408--417.

[30]

Huafeng Liu, Mingjie Zhou, Mingyang Song, Deqiang Ouyang, Yawen Li, Liping Jing, Jian Yu, and Michael K. Ng. 2023. Learning Hierarchical Preferences for Recommendation with Mixture Intention Neural Stochastic Processes. IEEE Trans. Knowl. Data Eng. (2023). https://doi.org/10.1109/TKDE.2023.3348493

Digital Library

[31]

Kangzheng Liu, Feng Zhao, Guandong Xu, Xianzhi Wang, and Hai Jin. 2023. RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation. In 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3--7, 2023. 1761--1774.

[32]

Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, and Vincent Cheng-Siong Lee. 2023. Anomaly Detection in Dynamic Graphs via Transformer. IEEE Trans. Knowl. Data Eng., Vol. 35, 12 (2023), 12081--12094.

Digital Library

[33]

Antonio Maccioni and Daniel J. Abadi. 2016. Scalable Pattern Matching over Compressed Graphs via Dedensification. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13--17, 2016. 1755--1764.

Digital Library

[34]

Emaad A. Manzoor, Sadegh M. Milajerdi, and Leman Akoglu. 2016. Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13--17, 2016. 1035--1044.

Digital Library

[35]

Jayanta Mondal and Amol Deshpande. 2012. Managing large dynamic graphs efficiently. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, Scottsdale, AZ, USA, May 20--24, 2012. 145--156.

Digital Library

[36]

Zara Nasar, Syed Waqar Jaffry, and Muhammad Kamran Malik. 2022. Named Entity Recognition and Relation Extraction: State-of-the-Art. ACM Comput. Surv., Vol. 54, 1 (2022), 20:1--20:39.

[37]

Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, and Yuxiao Dong. 2022. EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs. In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022. 794--803.

Digital Library

[38]

Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Meichun Hsu. 2001. PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth. In Proceedings of the 17th International Conference on Data Engineering, April 2--6, 2001, Heidelberg, Germany. 215--224.

[39]

Stephen Ranshous, Steve Harenberg, Kshitij Sharma, and Nagiza F. Samatova. 2016. A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations. In Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, Florida, USA, May 5--7, 2016. 189--197.

[40]

Xin Ren, Luyi Bai, Qianwen Xiao, and Xiangxi Meng. 2023. Hierarchical Self-Attention Embedding for Temporal Knowledge Graph Completion. In Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023. 2539--2547.

Digital Library

[41]

Jorma Rissanen. 1978. Modeling by shortest data description. Autom., Vol. 14, 5 (1978), 465--471.

Digital Library

[42]

Andrea Rossi, Donatella Firmani, Paolo Merialdo, and Tommaso Teofili. 2022. Kelpie: an Explainability Framework for Embedding-based Link Prediction Models. Proc. VLDB Endow., Vol. 15, 12 (2022), 3566--3569.

Digital Library

[43]

Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Müller, and Danai Koutra. 2019. Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket. In 2019 IEEE International Conference on Data Mining, ICDM 2019, Beijing, China, November 8--11, 2019. 528--537.

[44]

Mahsa Salehi and Lida Rashidi. 2018. A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]. SIGKDD Explor., Vol. 20, 1 (2018), 13--23.

Digital Library

[45]

Neil Shah, Danai Koutra, Tianmin Zou, Brian Gallagher, and Christos Faloutsos. 2015. TimeCrunch: Interpretable Dynamic Graph Summarization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10--13, 2015. 1055--1064.

Digital Library

[46]

Alisa Smirnova and Philippe Cudré-Mauroux. 2019. Relation Extraction Using Distant Supervision: A Survey. ACM Comput. Surv., Vol. 51, 5 (2019), 106:1--106:35.

Digital Library

[47]

Kumar Sricharan and Kamalika Das. 2014. Localizing anomalous changes in time-evolving graphs. In International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22--27, 2014. 1347--1358.

Digital Library

[48]

Jizhi Tang, Yansong Feng, and Dongyan Zhao. 2019. Learning to Update Knowledge Graphs by Reading News. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3--7, 2019. 2632--2641.

[49]

Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, and Guillaume Bouchard. 2017. Knowledge Graph Completion via Complex Tensor Factorization. J. Mach. Learn. Res., Vol. 18 (2017), 130:1--130:38.

[50]

Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, and Wen Gao. 2023. A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects. CoRR, Vol. abs/2308.02457 (2023).

[51]

Pei Wang, Junqi Wang, Pushpi Paranamana, and Patrick Shafto. 2020. A mathematical theory of cooperative communication. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual.

[52]

Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. 2020. TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16--20, 2020. 5730--5746.

[53]

Yinghui Wu, Shengqi Yang, Mudhakar Srivatsa, Arun Iyengar, and Xifeng Yan. 2013. Summarizing Answer Graphs Induced by Keyword Queries. Proc. VLDB Endow., Vol. 6, 14 (2013), 1774--1785.

Digital Library

[54]

Chengjin Xu, Yung-Yu Chen, Mojtaba Nayyeri, and Jens Lehmann. 2021. Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings. In NAACL-HLT. 2569--2578.

[55]

Chenjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. 2020. Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. In The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2--6, 2020, Proceedings, Part I. 654--671.

[56]

Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. 2020. TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8--13, 2020. 1583--1593.

[57]

Jinfa Yang, Xianghua Ying, Yongjie Shi, and Bowei Xing. 2024. Tensor decompositions for temporal knowledge graph completion with time perspective. Expert Syst. Appl., Vol. 237, Part A (2024), 121267.

Digital Library

[58]

Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel J. Kochenderfer, and Jure Leskovec. 2020. Handling Missing Data with Graph Representation Learning. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual.

[59]

Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. 2018. NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2018. 2672--2681.

Digital Library

[60]

Fu Zhang, Hongzhi Chen, Yuzhe Shi, Jingwei Cheng, and Jinghao Lin. 2024. Joint framework for tensor decomposition-based temporal knowledge graph completion. Inf. Sci., Vol. 654 (2024), 119853.

Digital Library

[61]

Jiasheng Zhang, Shuang Liang, Yongpan Sheng, and Jie Shao. 2022. Temporal knowledge graph representation learning with local and global evolutions. Knowl. Based Syst., Vol. 251 (2022), 109234.

Digital Library

[62]

Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao. 2019. AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10--16, 2019. 4419--4425.

[63]

Weiguo Zheng, Jeffrey Xu Yu, Lei Zou, and Hong Cheng. 2018. Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition. Proc. VLDB Endow., Vol. 11, 11 (2018), 1373--1386.

Digital Library

[64]

Xinyi Zhu, Liping Wang, Hao Xin, Xiaohan Wang, Zhifeng Jia, Jiyao Wang, Chunming Ma, and Yuxiang Zengt. 2023. T-FinKB: A Platform of Temporal Financial Knowledge Base Construction. In 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3--7, 2023. 3671--3674.

Cited By

Wang HXie WMa JLi DLi YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)FedSLS: Exploring Federated Aggregation in Saliency Latent SpaceProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681278(7182-7190)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681278

Index Terms

Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Temporal reasoning
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 6

SIGMOD

June 2024

792 pages

EISSN:2836-6573

DOI:10.1145/3709598

Editor:
Divyakant Agrawal
University of California, Santa Barbara, United States

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Published: 20 December 2024

Published in PACMMOD Volume 2, Issue 6

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Wang HXie WMa JLi DLi YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)FedSLS: Exploring Federated Aggregation in Saliency Latent SpaceProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681278(7182-7190)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681278

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