GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

• URL: http://arxiv.org/abs/2306.01951v7
• Date: Mon, 5 Feb 2024 16:12:30 GMT
• Title: GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
• Authors: Amit Roy, Juan Shu, Jia Li, Carl Yang, Olivier Elshocht, Jeroen Smeets and Pan Li
• Abstract summary: Graph Auto-Encoders (GAEs) encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. We propose GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors.
• Score: 36.56631787651942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains. A common method for GAD is Graph Auto-Encoders (GAEs), which encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. However, existing GAE models are primarily optimized for direct link reconstruction, resulting in nodes connected in the graph being clustered in the latent space. As a result, they excel at detecting cluster-type structural anomalies but struggle with more complex structural anomalies that do not conform to clusters. To address this limitation, we propose a novel solution called GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the local structure, self-attributes, and neighbor attributes, based on the corresponding node representation. By comparing the neighborhood reconstruction loss between anomalous nodes and normal nodes, GAD-NR can effectively detect any anomalies. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR is openly available. Importantly, the comparative analysis reveals that the existing methods perform well only in detecting one or two types of anomalies out of the three types studied. In contrast, GAD-NR excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.

Related papers

• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection [16.485082741239808]
  Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels. Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations. We propose a framework for Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD)
  arXiv  Detail & Related papers  (2024-04-25T07:09:05Z)
• Alleviating Structural Distribution Shift in Graph Anomaly Detection [70.1022676681496]
  Graph anomaly detection (GAD) is a challenging binary classification problem. Gallon neural networks (GNNs) benefit the classification of normals from aggregating homophilous neighbors. We propose a framework to mitigate the effect of heterophilous neighbors and make them invariant.
  arXiv  Detail & Related papers  (2024-01-25T13:07:34Z)
• ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection [84.0718034981805]
  We introduce a novel framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD) In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. In the next stage, the decoders are retrained for detection on the original graph.
  arXiv  Detail & Related papers  (2023-12-22T09:02:01Z)
• 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)
• Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment [22.769474986808113]
  Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph. We introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT) for GAD. ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods.
  arXiv  Detail & Related papers  (2022-12-02T11:21:48Z)
• ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness [70.60721571429784]
  We propose a new graph anomaly detection framework on attributed networks via substructure awareness (ARISE) ARISE focuses on the substructures in the graph to discern abnormalities. Experiments show that ARISE greatly improves detection performance compared to state-of-the-art attributed networks anomaly detection (ANAD) algorithms.
  arXiv  Detail & Related papers  (2022-11-28T12:17:40Z)
• Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation [61.39364567221311]
  Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs. We introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations.
  arXiv  Detail & Related papers  (2021-12-19T05:04:53Z)

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.