Community-based anomaly detection using spectral graph filtering

2022-01-24 · Rodrigo Francisquini, Ana Carolina Lorena, Mariá C. V. Nascimento

Several applications have a community structure where the nodes of the same community share similar attributes. Anomaly or outlier detection in networks is a relevant and widely studied research topic with applications in various domains. Despite a significant amount of anomaly detection frameworks, there is a dearth on the literature of methods that consider both attributed graphs and the community structure of the networks. This paper proposes a community-based anomaly detection algorithm using a spectral graph-based filter that includes the network community structure into the Laplacian matrix adopted as the basis for the Fourier transform. In addition, the choice of the cutoff frequency of the filter considers the number of communities found. In computational experiments, the proposed strategy, called SpecF, showed an outstanding performance in successfully identifying even discrete anomalies. SpecF is better than a baseline disregarding the community structure, especially for networks with a higher community overlapping. Additionally, we present a case study to validate the proposed method to study the dissemination of COVID-19 in the different districts of S\~ao Jos\'e dos Campos, Brazil.

📄 PDF Abstract BibTeX arXiv:2201.09936

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly Detection Outlier Detection

GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)

2025-08-21 · Wei Herng Choong, Jixing Liu, Ching-Yu Kao, Philip Sperl arxiv

Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly detection plays an important role. Recentl…

Graph Anomaly Detection Recommendation Systems Community Detection

CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection

2025-04-03 · Songran Bai, Xiaolong Zheng, Daniel Dajun Zeng

Graph Anomaly Detection (GAD) is critical in security-sensitive domains, yet faces reliability challenges: miscalibrated confidence estimation (underconfidence in normal nodes, overconfidence in anomalies), adversarial v…

Anomaly Detection Graph Anomaly Detection Prediction

On Consistency of Compressive Spectral Clustering

2017-02-12 · Muni Sreenivas Pydi, Ambedkar Dukkipati

Spectral clustering is one of the most popular methods for community detection in graphs. A key step in spectral clustering algorithms is the eigen decomposition of the $n{\times}n$ graph Laplacian matrix to extract its …

Clustering Community Detection Stochastic Block Model

Multi-Head Spectral-Adaptive Graph Anomaly Detection

2025-12-25 · Qingyue Cao, Bo Jin, Changwei Gong, Xin Tong 외 arxiv

Graph anomaly detection technology has broad applications in financial fraud and risk control. However, existing graph anomaly detection methods often face significant challenges when dealing with complex and variable ab…

Graph Anomaly Detection Graph Neural Network Contrastive Learning Fraud Detection

Rethinking Graph Neural Networks for Anomaly Detection

2022-05-31 · Jianheng Tang, Jiajin Li, Ziqi Gao, Jia Li

Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the…

Anomaly Detection Graph Anomaly Detection Graph Neural Network