Network Embedding for Cluster Analysis

Makarov, Ilya; Oborevich, Artem

doi:10.1109/cinti53070.2021.9668468

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2024

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Cited by 3 publications

References 22 publications

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Iterative Graph Embedding and Clustering

Oborevich,

Makarov

2023

Lecture Notes in Computer Science

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Iterative Graph Embedding and Clustering

Oborevich,

Makarov

2023

Lecture Notes in Computer Science

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Neural Drug Discovery

Shmyrev

Makarov²

2022

2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on R

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Anomaly Detection Based on GCNs and DBSCAN in a Large-Scale Graph

Retiti Diop Emane,

Song,

Lee

et al. 2024

Electronics

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Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs, adept at modeling intricate relationships, offer a flexible framework for capturing complex data structures. This paper proposes a novel anomaly detection approach, combining Graph Convolutional Networks (GCNs) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). GCNs, a specialized deep learning model for graph data, extracts meaningful node and edge representations by incorporating graph topology and attribute information. This facilitates learning expressive node embeddings capturing local and global structural patterns. For anomaly detection, DBSCAN, a density-based clustering algorithm effective in identifying clusters of varying densities amidst noise, is employed. By defining a minimum distance threshold and a minimum number of points within that distance, DBSCAN proficiently distinguishes normal graph elements from anomalies. Our approach involves training a GCN model on a labeled graph dataset, generating appropriately labeled node embeddings. These embeddings serve as input to DBSCAN, identifying clusters and isolating anomalies as noise points. The evaluation on benchmark datasets highlights the superior performance of our approach in anomaly detection compared to traditional methods. The fusion of GCNs and DBSCAN demonstrates a significant potential for accurate and efficient anomaly detection in graphs. This research contributes to advancing graph-based anomaly detection, with promising applications in domains where safeguarding data integrity and security is paramount.

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Network Embedding for Cluster Analysis

Cited by 3 publications

References 22 publications

Iterative Graph Embedding and Clustering

Iterative Graph Embedding and Clustering

Neural Drug Discovery

Anomaly Detection Based on GCNs and DBSCAN in a Large-Scale Graph

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