Graph Neural Networks
Graph Neural Networks (GNNs) work with data organized as graphs. A graph has nodes and edges. Nodes represent entities. Edges represent connections. GNNs learn patterns from both nodes and relationships.
Why GNNs Matter
Many real problems use graph structures: social networks, molecules, recommendation systems, and knowledge graphs. GNNs learn from connections and structure, not just raw features.
Core Idea
A GNN updates each node by collecting information from its neighbours. This method is called message passing. Over several layers, each node learns both local and global structure.
How GNNs Work
- Start with initial node features.
- Gather neighbour features.
- Aggregate them using sum, mean, or attention.
- Combine with the node’s own features.
- Apply a neural layer.
- Repeat across multiple GNN layers.
Message Passing
Message passing defines how nodes share information. Each GNN layer spreads features one hop away. After multiple layers, nodes capture multi-hop relationships across the graph.
Popular GNN Models
1. GCN
Graph Convolutional Network smooths features across the graph using a normalized adjacency matrix.
2. GAT
Graph Attention Network uses attention to weigh neighbours. Important nodes receive higher weights.
3. GraphSAGE
GraphSAGE samples neighbours and aggregates them. It works well for large graphs and inductive learning.
4. GIN
Graph Isomorphism Network offers strong expressive power. It distinguishes complex graph structures.
Common GNN Tasks
Node Classification
Predict a label for each node. Example: classifying users in a social network.
Graph Classification
Predict a label for the whole graph. Example: molecule toxicity prediction.
Link Prediction
Predict if a connection should exist. Example: recommending friends or items.
Recommendation
Use graph structure to suggest items based on relationships.
Strengths of GNNs
- Capture relational information
- Work on irregular, non-grid data
- Generalize well across graph structures
Limitations
- Slow on very large graphs
- Over-smoothing when using many layers
- Hard to interpret
Improving GNN Performance
- Use attention layers
- Limit depth to avoid over-smoothing
- Sample neighbours (GraphSAGE)
- Apply normalization and residual connections
GNNs in Moroccan Darija
Graph Neural Networks katkhddm m3a data li katsowwer graph. Nodes homa entities. Edges homa relations. Model kayt3llam men neighbours bach ybni representation qwiya.
Kif Kaykhddmo
- Kaybda b node features.
- Kayjma3 info men neighbours.
- Kayupdate node.
- Layers katsift info f jmi3 l graph.
Models
- GCN bach ysmout features.
- GAT b attention.
- GraphSAGE bach ysampeli neighbours.
Tasks
- Node classification.
- Graph classification.
- Link prediction.
Conclusion
GNNs learn from relational data using message passing. They support tasks in social networks, chemistry, recommendation systems, and more. They are a key part of modern deep learning.