Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟱𝟴 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 On One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published cvpr2022. 🔸 Github: https://lnkd.in/estn5ARy ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 ➡️ Nowadays, graph-structured data are increasingly used to model complex systems. ➡️ Meanwhile, detecting anomalies from graph has become a vital research problem of pressing societal concerns. ➡️ Anomaly detection is an unsupervised learning task of identifying rare data that differ from the majority. ➡️ As one of the dominant anomaly detection algorithms, One Class Support Vector Machine has been widely used to detect outliers. ➡️ However, those traditional anomaly detection methods lost their effectiveness in graph data. ➡️ Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data. ➡️ In this work, we propose One Class Graph Neural Network (OCGNN), a one-class classification framework for graph anomaly detection. ➡️ OCGNN is designed to combine the powerful representation ability of Graph Neural Networks along with the classical one-class objective. ➡️ Compared with other baselines, OCGNN achieves significant improvements in extensive experiments. #computervision #artificialintelligence #technology