<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gnn on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/gnn/</link><description>Recent content in Gnn on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/gnn/index.xml" rel="self" type="application/rss+xml"/><item><title>Graph</title><link>https://terms-en.ai-term-hub.com/en/terms/graph/</link><pubDate>Sat, 18 Jul 2026 09:32:53 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/graph/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>A graph is a fundamental data structure in AI comprising vertices (nodes) and edges (links) that denote relationships. Graph Neural Networks (GNNs) leverage this structure to perform learning on non-Euclidean data, such as social networks or molecular structures. Unlike grid-based data processed by CNNs, graphs allow for irregular connectivity and variable sizes. Graphs are essential for knowledge representation, reasoning, and modeling complex interactions where the relationship between items is as important as the items themselves.&lt;/p></description></item></channel></rss>