AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

• URL: http://arxiv.org/abs/2002.03665v2
• Date: Wed, 12 Feb 2020 10:26:44 GMT
• Title: AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
• Authors: Haoyi Fan, Fengbin Zhang, Zuoyong Li
• Abstract summary: 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.
• Score: 10.728863198129478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, AnomalyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.

Related papers

• HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection [3.389327931408283]
  Community detection plays a pivotal role in uncovering closely connected subgraphs. Previous research has effectively leveraged network topology and attribute information for attributed community detection. We propose HACD, a novel attributed community detection model based on heterogeneous graph attention networks.
  arXiv  Detail & Related papers  (2024-11-04T10:16:59Z)
• 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)
• Collaborative Graph Neural Networks for Attributed Network Embedding [63.39495932900291]
  Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. We propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for network embedding.
  arXiv  Detail & Related papers  (2023-07-22T04:52:27Z)
• 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)
• Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks [35.93516937521393]
  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.
  arXiv  Detail & Related papers  (2022-05-10T11:35:32Z)
• Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks [7.299729677753102]
  We propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space respectively. Experiments on the real-world attributed networks show that Dual-SVDAE consistently outperforms the state-of-the-arts.
  arXiv  Detail & Related papers  (2021-09-01T01:21:06Z)
• 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)
• GAGE: Geometry Preserving Attributed Graph Embeddings [34.25102483600248]
  This paper presents a novel approach for node embedding in attributed networks. It preserves the distances of both the connections and the attributes. An effective and lightweight algorithm is developed to tackle the learning task.
  arXiv  Detail & Related papers  (2020-11-03T02:07:02Z)
• On the use of local structural properties for improving the efficiency of hierarchical community detection methods [77.34726150561087]
  We study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection. We also check the performance impact of network prunings as an ancillary tactic to make hierarchical community detection more efficient.
  arXiv  Detail & Related papers  (2020-09-15T00:16:12Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Suppress and Balance: A Simple Gated Network for Salient Object Detection [89.88222217065858]
  We propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales.
  arXiv  Detail & Related papers  (2020-07-16T02:00:53Z)

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.