---
id: wiki-2026-0508-gnn
title: Graph Neural Networks (GNN)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [GNN, graph neural network, GCN, GAT, message passing, PyG, DGL]
duplicate_of: none
source_trust_level: A
confidence_score: 0.97
verification_status: applied
tags: [machine-learning, gnn, graph-neural-network, gcn, gat, message-passing, pyg]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: PyTorch Geometric / DGL
---

# Graph Neural Networks (GNN)

## 매 한 줄
> **"매 graph 의 의 의 message passing"**. 매 node + edge + global feature. 매 GCN (Kipf 2017), GAT, GraphSAGE, GIN, message-passing framework. 매 응용: 매 social, 매 drug, 매 molecule (AlphaFold), 매 traffic, 매 LLM 의 graph reasoning.

## 매 핵심

### 매 task
- **Node classification**: 매 단일 node label.
- **Link prediction**: 매 edge 의 의 likelihood.
- **Graph classification**: 매 entire graph.
- **Graph regression**.
- **Generation**: 매 graph generative.

### 매 layer family
- **GCN** (Kipf 2017): 매 spectral / message passing.
- **GAT**: 매 attention.
- **GraphSAGE**: 매 sampled neighborhood.
- **GIN** (Xu 2019): 매 most expressive.
- **Transformer-based**: GraphTransformer, Graphormer.
- **Message Passing NN** (general).

### 매 modern
- **Geometric DL** (Bronstein).
- **Equivariant GNN** (E(3), SE(3)).
- **AlphaFold-3** (geometric deep learning).
- **GNN + LLM** (graph reasoning).

### 매 응용
1. **Social network**: 매 fraud, recommendation.
2. **Molecule**: 매 drug, materials.
3. **Knowledge graph**: 매 reasoning.
4. **Traffic**: 매 ETA prediction.
5. **Recommender**.
6. **Combinatorial opt** (TSP, scheduling).

## 💻 패턴

### GCN (PyG)
```python
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class GCN(torch.nn.Module):
    def __init__(self, in_feat, hidden, n_classes):
        super().__init__()
        self.conv1 = GCNConv(in_feat, hidden)
        self.conv2 = GCNConv(hidden, n_classes)
    
    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x, edge_index))
        x = F.dropout(x, p=0.5, training=self.training)
        return self.conv2(x, edge_index)
```

### GAT (attention)
```python
from torch_geometric.nn import GATConv

class GAT(torch.nn.Module):
    def __init__(self, in_feat, hidden, n_heads=8):
        super().__init__()
        self.conv1 = GATConv(in_feat, hidden, heads=n_heads, dropout=0.6)
        self.conv2 = GATConv(hidden * n_heads, n_classes, heads=1, concat=False)
    
    def forward(self, x, edge_index):
        x = F.elu(self.conv1(x, edge_index))
        return self.conv2(x, edge_index)
```

### GraphSAGE (sampling)
```python
from torch_geometric.nn import SAGEConv
class GraphSAGE(torch.nn.Module):
    def __init__(self, in_feat, hidden, out_feat):
        super().__init__()
        self.conv1 = SAGEConv(in_feat, hidden, aggr='mean')
        self.conv2 = SAGEConv(hidden, out_feat, aggr='mean')
```

### Custom MessagePassing
```python
from torch_geometric.nn import MessagePassing

class CustomConv(MessagePassing):
    def __init__(self, in_feat, out_feat):
        super().__init__(aggr='mean')
        self.lin = torch.nn.Linear(in_feat, out_feat)
    
    def forward(self, x, edge_index):
        x = self.lin(x)
        return self.propagate(edge_index, x=x)
    
    def message(self, x_j):
        return x_j  # 매 from neighbor
    
    def update(self, aggr_out):
        return aggr_out
```

### Graph classification (read-out)
```python
from torch_geometric.nn import global_mean_pool

class GraphClassifier(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(in_feat, 64)
        self.conv2 = GCNConv(64, 64)
        self.classifier = torch.nn.Linear(64, n_classes)
    
    def forward(self, x, edge_index, batch):
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = global_mean_pool(x, batch)  # 매 graph-level
        return self.classifier(x)
```

### Link prediction
```python
import torch.nn as nn
class LinkPredictor(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = GCN(...)
        self.decoder = lambda src, dst: (src * dst).sum(-1)  # 매 dot product
    
    def forward(self, x, edge_index, edge_label_index):
        z = self.encoder(x, edge_index)
        src = z[edge_label_index[0]]
        dst = z[edge_label_index[1]]
        return self.decoder(src, dst)
```

### Sampling for large graphs (NeighborLoader)
```python
from torch_geometric.loader import NeighborLoader
loader = NeighborLoader(data, num_neighbors=[15, 10], batch_size=128, input_nodes=data.train_mask)

for batch in loader:
    out = model(batch.x, batch.edge_index)
    loss = F.cross_entropy(out[:batch.batch_size], batch.y[:batch.batch_size])
```

### Heterogeneous (HeteroData)
```python
from torch_geometric.data import HeteroData
data = HeteroData()
data['user'].x = user_feats
data['movie'].x = movie_feats
data['user', 'rates', 'movie'].edge_index = rate_edges

from torch_geometric.nn import to_hetero
model = to_hetero(model, data.metadata())
```

### Equivariant GNN (E(n)-EGNN)
```python
class EGNN(MessagePassing):
    def __init__(self, dim):
        super().__init__(aggr='mean')
        self.edge_mlp = nn.Sequential(nn.Linear(2*dim+1, dim), nn.SiLU(), nn.Linear(dim, dim))
        self.coord_mlp = nn.Linear(dim, 1)
    
    def forward(self, x, pos, edge_index):
        return self.propagate(edge_index, x=x, pos=pos)
    
    def message(self, x_i, x_j, pos_i, pos_j):
        rel_pos = pos_i - pos_j
        dist = (rel_pos ** 2).sum(-1, keepdim=True)
        edge_feat = self.edge_mlp(torch.cat([x_i, x_j, dist], -1))
        coord_msg = rel_pos * self.coord_mlp(edge_feat)
        return edge_feat, coord_msg
```

### Drug discovery (molecule)
```python
from torch_geometric.datasets import MoleculeNet
dataset = MoleculeNet(root='data', name='ESOL')
# 매 atom-level features + bond edges → solubility
```

### Knowledge graph (TransE)
```python
class TransE(nn.Module):
    def __init__(self, n_entities, n_relations, dim):
        super().__init__()
        self.entity_emb = nn.Embedding(n_entities, dim)
        self.relation_emb = nn.Embedding(n_relations, dim)
    
    def score(self, h, r, t):
        return -(self.entity_emb(h) + self.relation_emb(r) - self.entity_emb(t)).norm(dim=-1)
```

### Graph Transformer (Graphormer)
```python
class GraphTransformer(nn.Module):
    def __init__(self, dim, n_heads=8):
        super().__init__()
        self.attn = nn.MultiheadAttention(dim, n_heads)
        self.spatial_bias = nn.Embedding(MAX_DIST, n_heads)
    
    def forward(self, x, spatial_dist):
        # 매 attention with spatial bias
        bias = self.spatial_bias(spatial_dist)
        attn_out, _ = self.attn(x, x, x, attn_bias=bias)
        return attn_out
```

### GNN explainer
```python
from torch_geometric.explain import Explainer, GNNExplainer
explainer = Explainer(
    model=model, algorithm=GNNExplainer(epochs=200),
    explanation_type='model', node_mask_type='attributes',
    edge_mask_type='object',
)
explanation = explainer(data.x, data.edge_index, target=label)
```

## 매 결정 기준
| 상황 | Architecture |
|---|---|
| Default | GCN |
| Heterogeneous | HeteroData + GAT |
| Large graph | GraphSAGE + sampling |
| Most expressive | GIN |
| Spatial / molecule | EGNN / SchNet |
| Graph-level | + global pooling |
| Knowledge graph | TransE / RotatE |
| Long-range | GraphTransformer / Graphormer |

**기본값**: 매 PyG + 매 GCN/GAT baseline + 매 sampling for large + 매 EGNN for geometry + 매 explainer.

## 🔗 Graph
- 부모: [[Deep-Learning]] · [[Graph_Theory|Graph-Theory]]
- 변형: [[GCN]] · [[GAT]] · [[GIN]]
- 응용: [[Recommender-Systems]] · [[Knowledge-Graphs]]
- Adjacent: [[AlphaFold]]

## 🤖 LLM 활용
**언제**: 매 graph data. 매 social. 매 molecule. 매 KG.
**언제 X**: 매 sequence / image (use Transformer / CNN).

## ❌ 안티패턴
- **Over-smoothing** (deep GNN): 매 nodes converge.
- **No batching for large**: 매 OOM.
- **Ignore edge features**: 매 info lose.
- **Default attention 의 always**: 매 simple sometimes better.
- **No scaling for many classes**: 매 long-tail.

## 🧪 검증 / 중복
- Verified (Kipf GCN 2017, Xu GIN 2019, PyG/DGL docs, AlphaFold).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-26 | GNN auto |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — GCN/GAT/SAGE + 매 PyG / hetero / EGNN / link / explainer code |
