Multi-representations Space Separation based Graph-level Anomaly-aware Detection

• URL: http://arxiv.org/abs/2307.12994v1
• Date: Sat, 22 Jul 2023 01:57:08 GMT
• Title: Multi-representations Space Separation based Graph-level Anomaly-aware Detection
• Authors: Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang, Yue Zhang, Xuexiong Luo
• Abstract summary: The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. We propose a multi-representations space separation based graph-level anomaly-aware detection framework. Based on the distance error between the graph representations of the test graph and both normal and abnormal graph representation spaces, we can accurately determine whether the test graph is anomalous.
• Score: 8.39109029417354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem. The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies. Furthermore, abnormal graphs that have subtle differences from normal graphs are easily escaped detection by the existing methods. Thus, we propose a multi-representations space separation based graph-level anomaly-aware detection framework in this paper. To consider the different importance of node-level and graph-level anomalies, we design an anomaly-aware module to learn the specific weight between them in the abnormal graph evaluation process. In addition, we learn strictly separate normal and abnormal graph representation spaces by four types of weighted graph representations against each other including anchor normal graphs, anchor abnormal graphs, training normal graphs, and training abnormal graphs. Based on the distance error between the graph representations of the test graph and both normal and abnormal graph representation spaces, we can accurately determine whether the test graph is anomalous. Our approach has been extensively evaluated against baseline methods using ten public graph datasets, and the results demonstrate its effectiveness.

Related papers

• FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection [18.758250338590297]
  Unsupervised graph-level anomaly detection (UGAD) has attracted increasing interest due to its widespread application. We propose a Graph Normalizing Flows-driven Asymmetric Network For Unsupervised Graph-Level Anomaly Detection (FANFOLD)
  arXiv  Detail & Related papers  (2024-06-29T09:49:16Z)
• 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)
• 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)
• Self-Discriminative Modeling for Anomalous Graph Detection [27.595520991245014]
  We present a self-discriminative modeling framework for anomalous graph detection. We provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection. Surprisingly, our algorithms, though fully unsupervised, are able to significantly outperform supervised learning algorithms of anomalous graph detection.
  arXiv  Detail & Related papers  (2023-10-10T02:08:09Z)
• Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model [20.18156458266286]
  The goal of graph-level anomaly detection is to find abnormal graphs that significantly differ from others in a graph set. In this work, we first define anomalous graph information and adopt node-level and graph-level information differences to identify them. Two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.
  arXiv  Detail & Related papers  (2023-08-03T08:29:26Z)
• Graph-level Anomaly Detection via Hierarchical Memory Networks [19.217808857527743]
  We propose a novel approach called Hierarchical Memory Networks (HimNet) HimNet learns hierarchical memory modules -- node and graph memory modules -- via a graph autoencoder network architecture. The two modules are jointly optimized to detect both locally- and globally-anomalous graphs.
  arXiv  Detail & Related papers  (2023-07-03T04:57:53Z)
• DAGAD: Data Augmentation for Graph Anomaly Detection [57.92471847260541]
  This paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics.
  arXiv  Detail & Related papers  (2022-10-18T11:28:21Z)
• Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges [9.076649460696402]
  Graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks. This survey is the first comprehensive review of graph anomaly detection methods based on GNNs.
  arXiv  Detail & Related papers  (2022-09-29T16:47:57Z)
• 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)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)

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.