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		<title>When Language Models Meet Graph Networks: New Map Charts the Trust Fault Lines</title>
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		<pubDate>Wed, 07 Oct 2026 06:35:32 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[explainability]]></category>
		<category><![CDATA[fairness]]></category>
		<category><![CDATA[federated learning]]></category>
		<category><![CDATA[graph neural networks]]></category>
		<category><![CDATA[graph reasoning]]></category>
		<category><![CDATA[hallucination]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[privacy]]></category>
		<category><![CDATA[prompt injection]]></category>
		<category><![CDATA[reliability]]></category>
		<category><![CDATA[robustness]]></category>
		<category><![CDATA[trustworthy AI]]></category>
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					<description><![CDATA[A systematic review in Applied Intelligence introduces a two-axis taxonomy that maps how large language models enhance graph neural networks while exposing new risks to reliability, robustness, privacy, fairness, and explainability.]]></description>
		
		
		
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