Editing Graph Neural Networks (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a GNN-based recommendation system:

'''1. Graph construction'''
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Nodes:
├── Users (features: demographics, preferences)
└── Items (features: content embeddings, metadata)

Edges:
├── User-Item: interaction (click, purchase, rating)
├── Item-Item: co-purchase, co-view, semantic similarity
└── User-User: social connections (if available)

Edge features: timestamp, interaction type, rating value
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'''2. Model architecture (LightGCN for collaborative filtering)'''
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User/Item IDs → Embedding lookup
    ↓
[GNN layers: propagate embeddings across bipartite graph]
    ↓
[Sum pooling across all layers (jumping knowledge)]
    ↓
[User embedding · Item embedding → interaction score]
    ↓
[BPR loss: maximize score of interacted items vs. non-interacted]
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'''3. Scalability considerations'''
* Use FAISS for approximate nearest-neighbor retrieval of top-K items at inference
* Pre-compute item embeddings offline; only user embeddings need real-time update
* Cluster graph into subgraphs for mini-batch training (ClusterGCN, GraphSAINT)
* Cache neighborhood aggregations for stable, frequently-seen nodes

'''4. Cold start problem'''
* New users (no interaction history): fall back to content-based features or popularity
* New items: use item content embeddings as initial node features; propagate with GNN
* Inductive GNNs (GraphSAGE) generalize to new nodes naturally

[[Category:Artificial Intelligence]]
[[Category:Deep Learning]]
[[Category:Graph Neural Networks]]
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