GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Detection using Supervised and Unsupervised Techniques

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Feizi Fatemeh Zahra; RAHMANI HOSSEIN; Hosseinnia Amirhossein; Bagheri Asieh

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Detection using Supervised and Unsupervised Techniques

Author(s)

Feizi Fatemeh Zahra | RAHMANI HOSSEIN | Hosseinnia Amirhossein | Bagheri Asieh

Keywords

Anomaly Detection

Machine Learning

Graph Data

Ensemble Learning

Data Mining

Abstract

Recently, Graph Data has received a lot of attention as it is used to represent other data types including social networks, banking, security, financial, medical, and textual data. Therefore, Anomaly Detection in such data has become an important research area due to its ability to prevent adverse events like financial fraud, network intrusion, and social spam. Anomalies in Graph Data can occur at the node, edge, subgraph or graph level. Node anomalies can be due to unusual structure or attributes. Edge anomalies aim to identify unusual connections which are often unexpected or unusual relationships between real-world entities. The main challenge is detecting and categorizing such anomalies. In recent years, many computational methods have been proposed for Anomaly Detection in graphs using statistical analysis and Machine Learning approaches. In this research, we propose a framework called "GRAPH-Guard" for node-level Anomaly Detection using deep and Ensemble Learning algorithms. The framework is evaluated on heterogeneous attributed graphs and compared to other node Anomaly Detection algorithms quantitatively and qualitatively. The quantitative evaluation shows the proposed framework improves the area under the curve (AUC) by 4 points compared to the average AUC of previous works. It also improves the F1 score by 1 point compared to the best previous F1 score.

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APA: Copy

Feizi, Fatemeh Zahra, RAHMANI, HOSSEIN, Hosseinnia, Amirhossein, & Bagheri, Asieh. (2024). GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Detection using Supervised and Unsupervised Techniques. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147661/en

Vancouver: Copy

Feizi Fatemeh Zahra, RAHMANI HOSSEIN, Hosseinnia Amirhossein, Bagheri Asieh. GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Detection using Supervised and Unsupervised Techniques. 2024. Available from: https://sid.ir/paper/1147661/en

IEEE: Copy

Fatemeh Zahra Feizi, HOSSEIN RAHMANI, Amirhossein Hosseinnia, and Asieh Bagheri, “GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Detection using Supervised and Unsupervised Techniques,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147661/en

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