From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
- URL: http://arxiv.org/abs/2202.05525v2
- Date: Thu, 1 Aug 2024 01:42:10 GMT
- Title: From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
- Authors: Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen,
- Abstract summary: 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.
- Score: 26.973056364587766
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), thus inevitably limiting their capability in capturing anomalous patterns in complex graph data. To address this limitation, 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. In maximizing the agreements between instances at both the patch and context levels concurrently, we estimate the anomaly score of each node with a statistical anomaly estimator according to the degree of agreement from multiple perspectives. To further exploit a handful of ground-truth anomalies (few-shot anomalies) that may be collected in real-life applications, we further propose an extended algorithm, ANEMONE-FS, to integrate valuable information in our method. We conduct extensive experiments under purely unsupervised settings and few-shot anomaly detection settings, and we demonstrate that the proposed method ANEMONE and its variant ANEMONE-FS consistently outperform state-of-the-art algorithms on six benchmark datasets.
Related papers
- UMGAD: Unsupervised Multiplex Graph Anomaly Detection [40.17829938834783]
We propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD.
We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs.
Then, to weaken the influence of noise and redundant information on abnormal information extraction, we generate attribute-level and subgraph-level augmented-view graphs.
arXiv Detail & Related papers (2024-11-19T15:15:45Z) - 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) - Multitask Active Learning for Graph Anomaly Detection [48.690169078479116]
We propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE.
By coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies.
Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection.
arXiv Detail & Related papers (2024-01-24T03:43:45Z) - 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) - Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly
Detection [15.757864894708364]
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority.
We propose a novel few-shot Graph Anomaly Detection model called FMGAD.
We show that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.
arXiv Detail & Related papers (2023-11-17T07:49:20Z) - ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A
Unified Neural Network Approach [39.211176955683285]
We propose ADAMM, a novel graph neural network model that handles directed multi-graphs.
ADAMM fuses metadata and graph-level representation learning through an unsupervised anomaly detection objective.
arXiv Detail & Related papers (2023-11-13T14:19:36Z) - Towards Self-Interpretable Graph-Level Anomaly Detection [73.1152604947837]
Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection.
We propose a Self-Interpretable Graph aNomaly dETection model ( SIGNET) that detects anomalous graphs as well as generates informative explanations simultaneously.
arXiv Detail & Related papers (2023-10-25T10:10:07Z) - 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) - 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.