Mul-GAD: a semi-supervised graph anomaly detection framework via
aggregating multi-view information
- URL: http://arxiv.org/abs/2212.05478v1
- Date: Sun, 11 Dec 2022 11:34:34 GMT
- Title: Mul-GAD: a semi-supervised graph anomaly detection framework via
aggregating multi-view information
- Authors: Zhiyuan Liu, Chunjie Cao and Jingzhang Sun
- Abstract summary: We propose a multi-view fusion approach for graph anomaly detection (Mul-GAD)
The view-level fusion captures the extent of significance between different views, while the feature-level fusion makes full use of complementary information.
Compared with other state-of-the-art detection methods, we achieve a better detection performance and generalization in most scenarios.
- Score: 15.845749788061003
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Anomaly detection is defined as discovering patterns that do not conform to
the expected behavior. Previously, anomaly detection was mostly conducted using
traditional shallow learning techniques, but with little improvement. As the
emergence of graph neural networks (GNN), graph anomaly detection has been
greatly developed. However, recent studies have shown that GNN-based methods
encounter challenge, in that no graph anomaly detection algorithm can perform
generalization on most datasets. To bridge the tap, we propose a multi-view
fusion approach for graph anomaly detection (Mul-GAD). The view-level fusion
captures the extent of significance between different views, while the
feature-level fusion makes full use of complementary information. We
theoretically and experimentally elaborate the effectiveness of the fusion
strategies. For a more comprehensive conclusion, we further investigate the
effect of the objective function and the number of fused views on detection
performance. Exploiting these findings, our Mul-GAD is proposed equipped with
fusion strategies and the well-performed objective function. Compared with
other state-of-the-art detection methods, we achieve a better detection
performance and generalization in most scenarios via a series of experiments
conducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our code
is available at https://github.com/liuyishoua/Mul-Graph-Fusion.
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) - Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message
Passing and Hyperbolic Neural Networks [9.708651460086916]
In this paper, we revisit datasets and approaches for unsupervised node-level graph anomaly detection tasks.
Firstly, we introduce outlier injection methods that create more diverse and graph-based anomalies in graph datasets.
Secondly, we compare methods employing message passing against those without, uncovering the unexpected decline in performance.
arXiv Detail & Related papers (2024-03-06T19:42:34Z) - 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) - 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) - 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) - Normality Learning-based Graph Anomaly Detection via Multi-Scale
Contrastive Learning [61.57383634677747]
Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining.
Here, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation)
Notably, the proposed algorithm improves the detection performance (up to 5.89% AUC gain) compared with the state-of-the-art methods.
arXiv Detail & Related papers (2023-09-12T08:06:04Z) - 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) - 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) - 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.