refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-gnn
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title: Graph Neural Networks (GNN)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [GNN, graph neural network, GCN, GAT, message passing, PyG, DGL]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.97
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verification_status: applied
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tags: [machine-learning, gnn, graph-neural-network, gcn, gat, message-passing, pyg]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: PyTorch Geometric / DGL
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---
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# Graph Neural Networks (GNN)
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 task
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- **Node classification**: 매 단일 node label.
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- **Link prediction**: 매 edge 의 의 likelihood.
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- **Graph classification**: 매 entire graph.
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- **Graph regression**.
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- **Generation**: 매 graph generative.
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### 매 layer family
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- **GCN** (Kipf 2017): 매 spectral / message passing.
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- **GAT**: 매 attention.
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- **GraphSAGE**: 매 sampled neighborhood.
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- **GIN** (Xu 2019): 매 most expressive.
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- **Transformer-based**: GraphTransformer, Graphormer.
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- **Message Passing NN** (general).
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### 매 modern
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- **Geometric DL** (Bronstein).
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- **Equivariant GNN** (E(3), SE(3)).
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- **AlphaFold-3** (geometric deep learning).
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- **GNN + LLM** (graph reasoning).
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### 매 응용
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1. **Social network**: 매 fraud, recommendation.
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2. **Molecule**: 매 drug, materials.
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3. **Knowledge graph**: 매 reasoning.
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4. **Traffic**: 매 ETA prediction.
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5. **Recommender**.
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6. **Combinatorial opt** (TSP, scheduling).
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## 💻 패턴
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### GCN (PyG)
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```python
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import torch
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import torch.nn.functional as F
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from torch_geometric.nn import GCNConv
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class GCN(torch.nn.Module):
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def __init__(self, in_feat, hidden, n_classes):
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super().__init__()
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self.conv1 = GCNConv(in_feat, hidden)
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self.conv2 = GCNConv(hidden, n_classes)
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def forward(self, x, edge_index):
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x = F.relu(self.conv1(x, edge_index))
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x = F.dropout(x, p=0.5, training=self.training)
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return self.conv2(x, edge_index)
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```
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### GAT (attention)
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```python
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from torch_geometric.nn import GATConv
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class GAT(torch.nn.Module):
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def __init__(self, in_feat, hidden, n_heads=8):
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super().__init__()
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self.conv1 = GATConv(in_feat, hidden, heads=n_heads, dropout=0.6)
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self.conv2 = GATConv(hidden * n_heads, n_classes, heads=1, concat=False)
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def forward(self, x, edge_index):
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x = F.elu(self.conv1(x, edge_index))
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return self.conv2(x, edge_index)
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```
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### GraphSAGE (sampling)
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```python
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from torch_geometric.nn import SAGEConv
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class GraphSAGE(torch.nn.Module):
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def __init__(self, in_feat, hidden, out_feat):
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super().__init__()
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self.conv1 = SAGEConv(in_feat, hidden, aggr='mean')
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self.conv2 = SAGEConv(hidden, out_feat, aggr='mean')
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```
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### Custom MessagePassing
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```python
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from torch_geometric.nn import MessagePassing
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class CustomConv(MessagePassing):
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def __init__(self, in_feat, out_feat):
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super().__init__(aggr='mean')
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self.lin = torch.nn.Linear(in_feat, out_feat)
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def forward(self, x, edge_index):
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x = self.lin(x)
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return self.propagate(edge_index, x=x)
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def message(self, x_j):
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return x_j # 매 from neighbor
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def update(self, aggr_out):
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return aggr_out
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```
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### Graph classification (read-out)
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```python
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from torch_geometric.nn import global_mean_pool
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class GraphClassifier(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = GCNConv(in_feat, 64)
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self.conv2 = GCNConv(64, 64)
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self.classifier = torch.nn.Linear(64, n_classes)
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def forward(self, x, edge_index, batch):
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x = F.relu(self.conv1(x, edge_index))
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x = F.relu(self.conv2(x, edge_index))
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x = global_mean_pool(x, batch) # 매 graph-level
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return self.classifier(x)
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```
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### Link prediction
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```python
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import torch.nn as nn
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class LinkPredictor(nn.Module):
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def __init__(self):
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super().__init__()
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self.encoder = GCN(...)
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self.decoder = lambda src, dst: (src * dst).sum(-1) # 매 dot product
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def forward(self, x, edge_index, edge_label_index):
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z = self.encoder(x, edge_index)
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src = z[edge_label_index[0]]
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dst = z[edge_label_index[1]]
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return self.decoder(src, dst)
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```
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### Sampling for large graphs (NeighborLoader)
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```python
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from torch_geometric.loader import NeighborLoader
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loader = NeighborLoader(data, num_neighbors=[15, 10], batch_size=128, input_nodes=data.train_mask)
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for batch in loader:
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out = model(batch.x, batch.edge_index)
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loss = F.cross_entropy(out[:batch.batch_size], batch.y[:batch.batch_size])
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```
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### Heterogeneous (HeteroData)
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```python
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from torch_geometric.data import HeteroData
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data = HeteroData()
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data['user'].x = user_feats
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data['movie'].x = movie_feats
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data['user', 'rates', 'movie'].edge_index = rate_edges
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from torch_geometric.nn import to_hetero
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model = to_hetero(model, data.metadata())
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```
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### Equivariant GNN (E(n)-EGNN)
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```python
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class EGNN(MessagePassing):
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def __init__(self, dim):
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super().__init__(aggr='mean')
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self.edge_mlp = nn.Sequential(nn.Linear(2*dim+1, dim), nn.SiLU(), nn.Linear(dim, dim))
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self.coord_mlp = nn.Linear(dim, 1)
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def forward(self, x, pos, edge_index):
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return self.propagate(edge_index, x=x, pos=pos)
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def message(self, x_i, x_j, pos_i, pos_j):
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rel_pos = pos_i - pos_j
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dist = (rel_pos ** 2).sum(-1, keepdim=True)
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edge_feat = self.edge_mlp(torch.cat([x_i, x_j, dist], -1))
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coord_msg = rel_pos * self.coord_mlp(edge_feat)
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return edge_feat, coord_msg
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```
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### Drug discovery (molecule)
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```python
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from torch_geometric.datasets import MoleculeNet
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dataset = MoleculeNet(root='data', name='ESOL')
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# 매 atom-level features + bond edges → solubility
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```
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### Knowledge graph (TransE)
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```python
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class TransE(nn.Module):
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def __init__(self, n_entities, n_relations, dim):
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super().__init__()
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self.entity_emb = nn.Embedding(n_entities, dim)
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self.relation_emb = nn.Embedding(n_relations, dim)
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def score(self, h, r, t):
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return -(self.entity_emb(h) + self.relation_emb(r) - self.entity_emb(t)).norm(dim=-1)
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```
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### Graph Transformer (Graphormer)
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```python
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class GraphTransformer(nn.Module):
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def __init__(self, dim, n_heads=8):
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super().__init__()
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self.attn = nn.MultiheadAttention(dim, n_heads)
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self.spatial_bias = nn.Embedding(MAX_DIST, n_heads)
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def forward(self, x, spatial_dist):
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# 매 attention with spatial bias
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bias = self.spatial_bias(spatial_dist)
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attn_out, _ = self.attn(x, x, x, attn_bias=bias)
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return attn_out
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```
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### GNN explainer
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```python
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from torch_geometric.explain import Explainer, GNNExplainer
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explainer = Explainer(
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model=model, algorithm=GNNExplainer(epochs=200),
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explanation_type='model', node_mask_type='attributes',
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edge_mask_type='object',
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)
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explanation = explainer(data.x, data.edge_index, target=label)
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```
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## 매 결정 기준
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| 상황 | Architecture |
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| Default | GCN |
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| Heterogeneous | HeteroData + GAT |
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| Large graph | GraphSAGE + sampling |
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| Most expressive | GIN |
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| Spatial / molecule | EGNN / SchNet |
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| Graph-level | + global pooling |
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| Knowledge graph | TransE / RotatE |
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| Long-range | GraphTransformer / Graphormer |
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**기본값**: 매 PyG + 매 GCN/GAT baseline + 매 sampling for large + 매 EGNN for geometry + 매 explainer.
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## 🔗 Graph
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- 부모: [[Deep Learning]] · [[Graph_Theory|Graph-Theory]]
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- 변형: [[GCN]] · [[GAT]] · [[GIN]]
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- 응용: [[Recommender-Systems]] · [[Knowledge-Graphs]]
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- Adjacent: [[AlphaFold]]
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## 🤖 LLM 활용
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**언제**: 매 graph data. 매 social. 매 molecule. 매 KG.
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**언제 X**: 매 sequence / image (use Transformer / CNN).
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## ❌ 안티패턴
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- **Over-smoothing** (deep GNN): 매 nodes converge.
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- **No batching for large**: 매 OOM.
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- **Ignore edge features**: 매 info lose.
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- **Default attention 의 always**: 매 simple sometimes better.
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- **No scaling for many classes**: 매 long-tail.
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## 🧪 검증 / 중복
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- Verified (Kipf GCN 2017, Xu GIN 2019, PyG/DGL docs, AlphaFold).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-26 | GNN auto |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — GCN/GAT/SAGE + 매 PyG / hetero / EGNN / link / explainer code |
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