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		<title>Anomaly Detection: Balancing Structure and Attributes</title>
		<link>https://bioengineer.org/anomaly-detection-balancing-structure-and-attributes/</link>
		
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		<pubDate>Tue, 14 Oct 2025 12:47:45 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Anomaly detection in attributed networks]]></category>
		<category><![CDATA[contrastive learning frameworks]]></category>
		<category><![CDATA[network analysis performance metrics.]]></category>
		<category><![CDATA[real-world graph datasets]]></category>
		<category><![CDATA[structural-attribute balance]]></category>
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					<description><![CDATA[In recent advancements in machine learning and network analysis, a comprehensive framework has been proposed for detecting anomalies in attributed networks, specifically designed to address the challenges associated with identifying irregularities within diverse graph structures. An evaluation of the developed model was conducted using a diverse set of six real-world graph datasets comprising different social [&#8230;]]]></description>
		
		
		
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