--- title: Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply description: A visual, from-first-principles explanation of graph neural networks covering three core architectures: Graph Convolutional Networks (GCN), Message Passing... canonical: https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply | daily.dev og:description: A visual, from-first-principles explanation of graph neural networks covering three core architectures: Graph Convolutional Networks (GCN), Message Passing... og:url: https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop og:image: https://api.daily.dev/og/posts/q7ODNKdOP.png og:image:alt: Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply **[Towards Data Science](https://daily.dev/sources/tds)** · 11 min read · 2 upvotes · 0 comments ## Summary A visual, from-first-principles explanation of graph neural networks covering three core architectures: Graph Convolutional Networks (GCN), Message Passing Neural Networks (MPNN), and Graph Attention Networks (GAT). It walks through the GCN update rule using adjacency, feature, and weight matrices, explains fixes like self-loops and degree normalization (including Kipf & Welling's symmetric normalization), then extends the idea to edge-aware message passing in MPNNs and learned attention coefficients in GATs. It closes with a discussion of the oversmoothing problem in deep GNNs and mitigation techniques like skip connections and edge dropping. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between GCN and GAT in graph neural networks? GAT is a generalization of GCN that replaces fixed, degree-based normalization coefficients with learned attention weights. In GCN, the edge coefficient between nodes i and j is explicitly defined as 1 divided by the square root of the product of their degrees, while GAT learns this importance value, denoted alpha[i][j], directly from data, similar to attention in Transformers, and often uses multiple attention heads. _Comparing GNN architectures before picking one is easier with daily.dev's technical deep dives on hand._ ### What causes oversmoothing in deep graph neural networks? Oversmoothing happens when too many layers are stacked in a GNN, causing node feature representations to become nearly identical because repeated aggregation (like averaging) makes each node absorb more information from neighbors and converge toward them. This is why GNNs typically use only 2 to 4 layers, and techniques like skip connections and edge dropping help reduce the effect. _Developers tuning GNN depth can keep architecture pitfalls like this bookmarked on daily.dev._ ### Why do message passing neural networks (MPNN) require more memory than GAT? MPNNs require more computation and memory because the messages passed between connected nodes are learned vectors for each edge, computed via a message function and aggregated via a readout function, both typically implemented as small MLPs. GATs are more memory-efficient because their learned attention coefficients are simply scalar values per edge rather than full vectors, making GATs preferable for larger graphs. _daily.dev helps developers weighing memory trade-offs across graph model architectures stay informed._ ## Similar posts on daily.dev - [A Deeper Dive into Graph Neural Networks](https://daily.dev/posts/a-deeper-dive-into-graph-neural-networks-bjxzik1ur) · DigitalOcean Community · 0 upvotes · 0 comments - [CNNs, RNNs, and Transformers Explained: A Mental Model for Key Deep Learning Concepts](https://daily.dev/posts/cnns-rnns-and-transformers-explained-a-mental-model-for-key-deep-learning-concepts-pxbxlxvct) · freeCodeCamp · 2 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#deep-learning](https://daily.dev/tags/deep-learning), [#neural-networks](https://daily.dev/tags/neural-networks) [View this post on daily.dev](https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply","url":"https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop"},"datePublished":"2026-09-02T12:50:49.464Z","dateModified":"2026-09-02T13:11:09.603Z","description":"A visual, from-first-principles explanation of graph neural networks covering three core architectures: Graph Convolutional Networks (GCN), Message Passing...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/5c35c19d8e3a18d76db205f917c1eced?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/5c35c19d8e3a18d76db205f917c1eced?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":2},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,deep-learning,neural-networks","timeRequired":"PT11M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/graph-neural-networks-gcn-mpnn-and-gat-explained-simply-q7odnkdop#faq","mainEntity":[{"@type":"Question","name":"What is the difference between GCN and GAT in graph neural networks?","acceptedAnswer":{"@type":"Answer","text":"GAT is a generalization of GCN that replaces fixed, degree-based normalization coefficients with learned attention weights. In GCN, the edge coefficient between nodes i and j is explicitly defined as 1 divided by the square root of the product of their degrees, while GAT learns this importance value, denoted alpha[i][j], directly from data, similar to attention in Transformers, and often uses multiple attention heads. Comparing GNN architectures before picking one is easier with daily.dev's technical deep dives on hand."}},{"@type":"Question","name":"What causes oversmoothing in deep graph neural networks?","acceptedAnswer":{"@type":"Answer","text":"Oversmoothing happens when too many layers are stacked in a GNN, causing node feature representations to become nearly identical because repeated aggregation (like averaging) makes each node absorb more information from neighbors and converge toward them. This is why GNNs typically use only 2 to 4 layers, and techniques like skip connections and edge dropping help reduce the effect. Developers tuning GNN depth can keep architecture pitfalls like this bookmarked on daily.dev."}},{"@type":"Question","name":"Why do message passing neural networks (MPNN) require more memory than GAT?","acceptedAnswer":{"@type":"Answer","text":"MPNNs require more computation and memory because the messages passed between connected nodes are learned vectors for each edge, computed via a message function and aggregated via a readout function, both typically implemented as small MLPs. GATs are more memory-efficient because their learned attention coefficients are simply scalar values per edge rather than full vectors, making GATs preferable for larger graphs. daily.dev helps developers weighing memory trade-offs across graph model architectures stay informed."}}]} ```