DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
- URL: http://arxiv.org/abs/2412.16788v2
- Date: Mon, 20 Jan 2025 20:17:59 GMT
- Title: DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
- Authors: Hossein Rafieizadeh, Hadi Zare, Mohsen Ghassemi Parsa, Hadi Davardoust, Meshkat Shariat Bagheri,
- Abstract summary: Anomaly detection using a network-based approach is one of the most efficient ways to identify abnormal events.
This paper introduces DCOR, a novel approach on attributed networks that integrates reconstruction-based anomaly detection with Contrastive Learning.
- Score: 5.382679710017696
- License:
- Abstract: Anomaly detection using a network-based approach is one of the most efficient ways to identify abnormal events such as fraud, security breaches, and system faults in a variety of applied domains. While most of the earlier works address the complex nature of graph-structured data and predefined anomalies, the impact of data attributes and emerging anomalies are often neglected. This paper introduces DCOR, a novel approach on attributed networks that integrates reconstruction-based anomaly detection with Contrastive Learning. Utilizing a Graph Neural Network (GNN) framework, DCOR contrasts the reconstructed adjacency and feature matrices from both the original and augmented graphs to detect subtle anomalies. We employed comprehensive experimental studies on benchmark datasets through standard evaluation measures. The results show that DCOR significantly outperforms state-of-the-art methods. Obtained results demonstrate the efficacy of proposed approach in attributed networks with the potential of uncovering new patterns of anomalies.
Related papers
- Higher-order Structure Based Anomaly Detection on Attributed Networks [25.94747823510297]
We present a higher-order structure based anomaly detection (GUIDE) method.
We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures.
We also design a graph attention layer to evaluate the significance of neighbors to nodes.
arXiv Detail & Related papers (2024-06-07T07:02:50Z) - 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) - 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) - Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe.
GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
arXiv Detail & Related papers (2023-02-06T16:38:43Z) - 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) - 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) - Regularized Cycle Consistent Generative Adversarial Network for Anomaly
Detection [5.457279006229213]
We propose a new Regularized Cycle Consistent Generative Adversarial Network (RCGAN) in which deep neural networks are adversarially trained to better recognize anomalous samples.
Experimental results on both real-world and synthetic data show that our model leads to significant and consistent improvements on previous anomaly detection benchmarks.
arXiv Detail & Related papers (2020-01-18T03:35:05Z)
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.