Robust Anomaly Detection with Graph Neural Networks using Controllability

• URL: http://arxiv.org/abs/2507.13954v1
• Date: Fri, 18 Jul 2025 14:21:10 GMT
• Title: Robust Anomaly Detection with Graph Neural Networks using Controllability
• Authors: Yifan Wei, Anwar Said, Waseem Abbas, Xenofon Koutsoukos,
• Abstract summary: Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data.<n>We propose two novel approaches to integrate average controllability into graph-based frameworks.
• Score: 2.354377098854566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data and the inherently imbalanced nature of anomalous versus benign samples. Graph-based machine learning models have emerged as a promising solution that combines attribute and relational data to uncover intricate patterns. However, the scarcity of anomalous data exacerbates the challenge, which requires innovative strategies to enhance model learning with limited information. In this paper, we hypothesize that the incorporation of the influence of the nodes, quantified through average controllability, can significantly improve the performance of anomaly detection. We propose two novel approaches to integrate average controllability into graph-based frameworks: (1) using average controllability as an edge weight and (2) encoding it as a one-hot edge attribute vector. Through rigorous evaluation on real-world and synthetic networks with six state-of-the-art baselines, our proposed methods demonstrate improved performance in identifying anomalies, highlighting the critical role of controllability measures in enhancing the performance of graph machine learning models. This work underscores the potential of integrating average controllability as additional metrics to address the challenges of anomaly detection in sparse and imbalanced datasets.

Related papers

• Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective [54.605073936695575]
  Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection.<n>Existing methods rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality.<n>The presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process.<n>We propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process.
  arXiv  Detail & Related papers  (2025-05-23T15:05:56Z)
• UMGAD: Unsupervised Multiplex Graph Anomaly Detection [40.17829938834783]
  We propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD.<n>We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs.<n>Then, to further extract abnormal information, we generate attribute-level and subgraph-level augmented-view graphs.
  arXiv  Detail & Related papers  (2024-11-19T15:15:45Z)
• Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization [0.0]
  We present a self-adapting anomaly detection framework for joint learning of (a) discrete hypergraph structure and (b) modeling the temporal trends and spatial relations among the interdependent sensors. The framework exploits the relational inductive biases in the hypergraph-structured data to learn the pointwise single-step-ahead forecasts. It derives the anomaly information propagation-based computational hypergraphs for root cause analysis and provides recommendations through an offline, optimal predictive control policy.
  arXiv  Detail & Related papers  (2024-08-21T06:04:02Z)
• Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning [1.3756846638796]
  We propose an imbalanced GLAD method via counterfactual augmentation and feature learning. We apply the model to brain disease datasets, which can prove the capability of our work.
  arXiv  Detail & Related papers  (2024-07-13T13:40:06Z)
• Develop End-to-End Anomaly Detection System [3.130722489512822]
  Anomaly detection plays a crucial role in ensuring network robustness. We propose an end-to-end anomaly detection model development pipeline. We demonstrate the efficacy of the framework by way of introducing and bench-marking a new forecasting model.
  arXiv  Detail & Related papers  (2024-02-01T09:02:44Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• GADY: Unsupervised Anomaly Detection on Dynamic Graphs [18.1896489628884]
  We propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets.
  arXiv  Detail & Related papers  (2023-10-25T05:27:45Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach [26.973056364587766]
  Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. We propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short) By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph.
  arXiv  Detail & Related papers  (2022-02-11T09:45:11Z)
• Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection [14.631674952942207]
  We propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD) Our method constructs different contextual subgraphs based on a target node and employs two modules, generative attribute regression and multi-view contrastive learning for anomaly detection. We conduct extensive experiments on six benchmark datasets and the results demonstrate that our method outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-08-23T02:15:21Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.