Generative Semi-supervised Graph Anomaly Detection

• URL: http://arxiv.org/abs/2402.11887v4
• Date: Tue, 28 May 2024 08:31:28 GMT
• Title: Generative Semi-supervised Graph Anomaly Detection
• Authors: Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang,
• Abstract summary: This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal. We propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the normal nodes. GGAD is designed to leverage two important priors about the anomaly nodes -- asymmetric local affinity and egocentric closeness.
• Score: 42.02691404704764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper, we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the normal nodes. The key idea is to generate pseudo anomaly nodes, referred to as 'outlier nodes', for providing effective negative node samples in training a discriminative one-class classifier. The main challenge here lies in the lack of ground truth information about real anomaly nodes. To address this challenge, GGAD is designed to leverage two important priors about the anomaly nodes -- asymmetric local affinity and egocentric closeness -- to generate reliable outlier nodes that assimilate anomaly nodes in both graph structure and feature representations. Comprehensive experiments on six real-world GAD datasets are performed to establish a benchmark for semi-supervised GAD and show that GGAD substantially outperforms state-of-the-art unsupervised and semi-supervised GAD methods with varying numbers of training normal nodes. Code will be made available at https://github.com/mala-lab/GGAD.

Related papers

• Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts [21.05107001235223]
  Graph anomaly detection (GAD) aims to identify nodes in a graph that significantly deviate from normal patterns. Existing GAD methods, whether supervised or unsupervised, are one-model-for-one-dataset approaches. We propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model.
  arXiv  Detail & Related papers  (2024-10-18T22:23:59Z)
• Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for More Efficient Dissimilarity Preservation in Graph Embeddings [8.858596502294471]
  We show that node-wise repulsion is, in aggregate, an approximate re-centering of the node embedding dimensions. We propose an algorithm augmentation framework that speeds up any existing algorithm, supervised or unsupervised.
  arXiv  Detail & Related papers  (2024-04-30T19:43:01Z)
• 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)
• 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)
• GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction [36.56631787651942]
  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.
  arXiv  Detail & Related papers  (2023-06-02T23:23:34Z)
• Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection [21.731515133452977]
  We show that normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker than normal nodes. This anomaly-discriminative property is ignored by existing anomaly detection methods that are typically built using a conventional anomaly detection objective. We propose Truncated Affinity Maximization (TAM) that learns tailored node representations for our anomaly measure by maximizing the local affinity of nodes to their neighbors.
  arXiv  Detail & Related papers  (2023-05-29T08:39:16Z)
• 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)
• ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization [80.90206641975375]
  This paper focuses on improving the performance of GNNs via normalization. By studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs. The $scale$ operation of ResNorm reshapes the node-wise standard deviation (NStd) distribution so as to improve the accuracy of tail nodes.
  arXiv  Detail & Related papers  (2022-06-16T13:49:09Z)
• 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)
• Graph Inference Learning for Semi-supervised Classification [50.55765399527556]
  We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification. For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes. Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-17T02:52:30Z)

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.