mlsanityData to 7 Oct 2026

A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs

arXiv 2408.06121 ↗First posted 12 Aug 2024cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we explore different approaches to anomaly detection on dynamic knowledge graphs, specifically in a Micro-services environment for Kubernetes applications. Our approach explores three dynamic knowledge graph representations: sequential data, hierarchical data and inter-service dependency data, with each representation incorporating increasingly complex structural information of dynamic knowledge graph. Different machine learning and deep learning models are tested on these representations. We empirically analyse their performance and propose an approach based on ensemble learning of these models. Our approach significantly outperforms the baseline on the ISWC 2024 Dynamic Knowledge Graph Anomaly Detection dataset, providing a robust solution for anomaly detection in dynamic complex data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 12 Aug 2024.

Builds on

18 of 40 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  7. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  8. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  9. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  10. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  11. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  12. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
6 more
  1. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 57
  2. 2020Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphscited by 55
  3. 2022Raising the Bar in Graph-level Anomaly Detectioncited by 45
  4. 2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
  5. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  6. 2023Multivariate Time Series Anomaly Detection via Dynamic Graph Forecastingcited by 7

Built on by

Not cited yet.