Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks

• URL: http://arxiv.org/abs/2205.04816v1
• Date: Tue, 10 May 2022 11:35:32 GMT
• Title: Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
• Authors: Jiaqiang Zhang, Senzhang Wang, Songcan Chen
• Abstract summary: This paper proposes a self-supervised learning framework that jointly optimize a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
• Score: 35.93516937521393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.

Related papers

• Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks [0.0]
  We deploy decoupled GNNs to overcome the issue of capturing nonlinear network information. For node representation learning, we develop a GNN architecture with two modules for aggregating node feature information. The findings demonstrate that decoupled training along with the global context enhanced representation of the nodes is superior to the state-of-the-art models in terms of AUC.
  arXiv  Detail & Related papers  (2024-09-04T21:54:07Z)
• Fundamental limits of community detection from multi-view data: multi-layer, dynamic and partially labeled block models [7.778975741303385]
  We study community detection in multi-view data in modern network analysis. We characterize the mutual information between the data and the latent parameters. We introduce iterative algorithms based on Approximate Message Passing for community detection.
  arXiv  Detail & Related papers  (2024-01-16T07:13:32Z)
• SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks [19.09775548036214]
  Anomaly detection on attributed networks aims to find the nodes whose behaviors are significantly different from other majority nodes. We present a novel contrastive learning framework for anomaly detection on attributed networks, textbfSCALA, aiming to improve the embedding quality of the network. Extensive experiments are conducted on five benchmark real-world datasets and the results show that SCALA consistently outperforms all baseline methods significantly.
  arXiv  Detail & Related papers  (2024-01-03T08:51:18Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• 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)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection [54.76993389109327]
  Unsupervised anomaly detection aims to build models to detect unseen anomalies by only training on the normal data. We propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges. AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations.
  arXiv  Detail & Related papers  (2022-01-03T03:40:21Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks [10.728863198129478]
  Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes. We propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE) Anomaly can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives.
  arXiv  Detail & Related papers  (2020-02-10T11:32:23Z)

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.