# Graph Neural Networks: GCN, MPNN, and GAT Explained Simply Discover specialized neural network architectures designed to process graph structures. - **Source:** [Towards Data Science](https://towardsdatascience.com) - **Published:** 2026-09-02 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/ded24394-634b-4a9f-b64b-fb8c93bb7704 ## Introduction to Graph Neural Networks Graph Neural Networks (GNNs) are a specialized type of neural network designed to process objects and their relationships in graph structures. Many data types like molecules, social networks, and traffic maps can be represented as graphs. GNNs aim to leverage the valuable contextual information these structures contain to achieve better results. For instance, they have been successfully applied to complex problems like antibiotic discovery. ## Basic Working Principle of GNNs GNNs process each node and its neighbors to create new feature vectors. This operation uses an adjacency matrix (A), feature matrix (H), and learnable transformation matrix (W). The product AH gives the sum of neighbors' features for each node. This result is then multiplied by W and transformed through a non-linear function (σ) to obtain updated features. ## GNN Layers and Architecture GNNs typically consist of 2-4 layers. More layers can lead to oversmoothing problems. Each layer transforms feature vectors from the previous layer by aggregating neighbor information. This process runs in parallel for all nodes, and the resulting feature vectors may differ in dimension from the input. This flexibility allows GNNs to be applied to graphs with different structures. ## Importance of the Adjacency Matrix The adjacency matrix (A) defines the graph structure, where A[i][j]=1 indicates a connection between nodes i and j. However, the basic approach doesn't consider a node's own features. To solve this, diagonal elements of A are set to 1. This modification allows each node to include its own features in the computation. ## Normalization and Scaling Issues Matrix multiplication can change feature scales. This is addressed using normalization with the degree matrix (D), where D[i][i] counts the neighbors (including itself) of node i. Kipf and Welling (2017) proposed symmetric normalization by applying the inverse square root of D to both sides. This balances feature scales and produces more stable results. ## Generalization Capability of GNNs One of GNNs' strongest features is their ability to generalize to unseen graph structures. The learned matrix W is shared across all nodes, making GNNs applicable to any graph regardless of node or edge count. For example, a GNN trained on specific molecule types can classify new molecular structures it hasn't encountered during training. ## GNN Variants: GCN Graph Convolutional Networks (GCN) are among the most common GNN types. GCN updates each node's features using a weighted average of its neighbors' features, employing symmetric normalization to maintain feature scales. GCNs perform particularly well in node classification tasks but may face oversmoothing with increasing layers. ## GNN Variants: MPNN Message Passing Neural Networks (MPNN) provide a general GNN framework. In MPNNs, each node receives messages from neighbors and updates its state by aggregating them. This approach is highly flexible, allowing customization through different message and update functions. MPNNs have shown success in quantum chemistry and molecular property prediction. ## GNN Variants: GAT Graph Attention Networks (GAT) integrate attention mechanisms into GNNs. GAT learns to assign different weights to neighbors, identifying which connections are more important. This helps the model focus on the graph's most significant structural features. GATs outperform traditional GCNs particularly on heterogeneous graphs with diverse relationship types. ## Applications of GNNs GNNs have been successfully applied across various domains. They predict user behavior in social network analysis, determine optimal routes in traffic management, and estimate molecular properties in drug discovery. Other applications include recommendation systems and knowledge graph processing. GNNs' flexible architecture enables adaptation to different data types. ## Future of GNNs and Challenges GNNs are playing an increasingly important role in graph data processing. However, challenges remain, including computational resources for large-scale graphs, oversmoothing, and model interpretability. Future developments may focus on more efficient architectures and broader applications, particularly in bioinformatics and materials science where GNNs could drive significant advancements. ## Sources covering this story - https://towardsdatascience.com/graph-neural-networks-gcn-mpnn-and-gat-explained-simply/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._