{"@attributes":{"version":"2.0"},"channel":{"title":"Graph on Oriol Al\u00e0s Cerc\u00f3s","link":"https:\/\/oriolac.github.io\/tags\/graph\/","description":"Recent content in Graph on Oriol Al\u00e0s Cerc\u00f3s","generator":"Hugo -- 0.150.0","language":"en-us","copyright":"Oriol Al\u00e0s Cerc\u00f3s","lastBuildDate":"Wed, 24 Jun 2026 17:10:23 +0100","item":{"title":"Under the Hood of Graph Neural Networks: Message Passing, Over-Smoothing and Attention","link":"https:\/\/oriolac.github.io\/posts\/20260624-gnns\/","pubDate":"Wed, 24 Jun 2026 17:10:23 +0100","guid":"https:\/\/oriolac.github.io\/posts\/20260624-gnns\/","description":"

In this post we will present an introduction of how Spatial Graph Neural Networks (GNNs)<\/strong> or Graph Convolutional\nNeural\nNetworks (GCNs)<\/strong> work. First, we are going to define graph data structures. Then, we are going to explain the mechanism\non GNNs. And finally, we will explain how to incorporate an attention mechanism in the network.<\/p>\n

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\n<\/svg>\nNotation of GNNs<\/p>\n

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During the whole text, we will use the notation of GNN as Spatial Graph Neural Network, although GCN or Graph\nConvolutional Neural Network is another notation to say it. There are other types of GNNs like Spectral Graph Neural\nNetworks, but in this post we will focus on the first mentioned ones.<\/p>"}}}