2nd/10_Wiki/Topics/Architecture/Graph_Theory.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-graph-theory	Graph Theory	10_Wiki/Topics	verified	self	그래프 이론 Graphs	none	A	0.9	applied	algorithms graphs cs		2026-05-10	pending	language framework python networkx

Graph Theory

매 한 줄

"매 vertex + edge 의 abstraction 으로 매 거의 모든 관계를 model". 매 Euler (1736 Königsberg) 부터 매 modern Graph Neural Network (2026 GraphSAGE, GAT, Graph Transformer) 까지 — 매 search engine PageRank, social network, dependency graph, GNN, knowledge graph 의 foundation.

매 핵심

매 representations

• Adjacency matrix: O(V²) space, O(1) edge query — 매 dense graph.
• Adjacency list: O(V+E) space — 매 sparse 일반.
• Edge list: 매 streaming / disk-based.
• CSR (Compressed Sparse Row): 매 cache-friendly, GNN framework 의 표준.

매 핵심 algorithm

• BFS O(V+E) — 매 unweighted shortest path, level order.
• DFS O(V+E) — 매 cycle detection, topo sort, SCC.
• Dijkstra O((V+E) log V) — 매 non-negative weighted shortest path.
• Bellman-Ford O(VE) — 매 negative weight allowed.
• Floyd-Warshall O(V³) — 매 all-pairs.
• Union-Find ~ O(α(V)) — 매 connectivity, Kruskal MST.
• Topological sort — 매 DAG ordering.
• PageRank — 매 power iteration on stochastic matrix.

매 응용

1. Routing / map — 매 OSRM, Valhalla, Google Maps.
2. Dependency resolution — npm, cargo, Bazel.
3. GNN — 매 fraud detection, drug discovery, recommendation (Pinterest PinSage).
4. Knowledge graph — 매 RAG / LLM grounding.

💻 패턴

Adjacency list (Python)

from collections import defaultdict
g: dict[int, list[int]] = defaultdict(list)
for u, v in edges:
    g[u].append(v); g[v].append(u)

BFS shortest path

from collections import deque
def bfs(g, s, t):
    q = deque([(s, 0)]); seen = {s}
    while q:
        u, d = q.popleft()
        if u == t: return d
        for v in g[u]:
            if v not in seen: seen.add(v); q.append((v, d+1))
    return -1

Dijkstra (heap)

import heapq
def dijkstra(g, s):
    dist = {s: 0}; pq = [(0, s)]
    while pq:
        d, u = heapq.heappop(pq)
        if d > dist[u]: continue
        for v, w in g[u]:
            nd = d + w
            if nd < dist.get(v, float("inf")):
                dist[v] = nd; heapq.heappush(pq, (nd, v))
    return dist

Topological sort (Kahn)

from collections import deque
def topo(n, edges):
    g = [[] for _ in range(n)]; indeg = [0]*n
    for u, v in edges: g[u].append(v); indeg[v] += 1
    q = deque(i for i in range(n) if indeg[i] == 0); out = []
    while q:
        u = q.popleft(); out.append(u)
        for v in g[u]:
            indeg[v] -= 1
            if indeg[v] == 0: q.append(v)
    return out if len(out) == n else None  # cycle

Union-Find (path compression + rank)

class DSU:
    def __init__(self, n): self.p = list(range(n)); self.r = [0]*n
    def find(self, x):
        while self.p[x] != x:
            self.p[x] = self.p[self.p[x]]; x = self.p[x]
        return x
    def union(self, a, b):
        ra, rb = self.find(a), self.find(b)
        if ra == rb: return False
        if self.r[ra] < self.r[rb]: ra, rb = rb, ra
        self.p[rb] = ra
        if self.r[ra] == self.r[rb]: self.r[ra] += 1
        return True

NetworkX (Python prototyping)

import networkx as nx
G = nx.DiGraph(); G.add_weighted_edges_from([("a","b",1), ("b","c",2)])
print(nx.shortest_path(G, "a", "c", weight="weight"))
print(nx.pagerank(G, alpha=0.85))

GNN (PyTorch Geometric)

import torch
from torch_geometric.nn import GCNConv
class GCN(torch.nn.Module):
    def __init__(self, d_in, d_h, d_out):
        super().__init__()
        self.c1 = GCNConv(d_in, d_h); self.c2 = GCNConv(d_h, d_out)
    def forward(self, x, edge_index):
        x = self.c1(x, edge_index).relu()
        return self.c2(x, edge_index)

매 결정 기준

상황	Approach
Unweighted shortest	BFS
Non-negative weighted	Dijkstra
Negative weight	Bellman-Ford / Johnson
All-pairs, dense, V<500	Floyd-Warshall
MST	Kruskal (DSU) / Prim (heap)
DAG scheduling	Topological sort
Cycle / SCC	DFS + Tarjan / Kosaraju
Massive web graph	Pregel-style (GraphX, Giraph)
Learning on graph	GNN (PyG, DGL)

기본값: prototype 은 NetworkX, production 은 graph-tool / igraph / 직접 CSR.

🔗 Graph

• 변형: DAG
• 응용: Shortest Path · GNN · Knowledge Graph

🤖 LLM 활용

언제: 매 problem 을 graph 로 model 가능한지 reasoning, 매 algorithm 선택. 언제 X: 매 V, E 가 매우 작은 brute-force 충분 case — 매 over-engineering.

❌ 안티패턴

• Adjacency matrix on sparse: 매 V=10⁶ 에 V² 메모리 폭발.
• Recursive DFS on deep graph (Python): 매 stack overflow — iterative.
• Dijkstra with negative edges: 매 wrong answer — Bellman-Ford.
• Re-running BFS from each node for diameter: 매 BFS×V — multi-source / approx 사용.
• Forgetting visited set: 매 BFS/DFS 무한 loop.

🧪 검증 / 중복

• Verified (CLRS Introduction to Algorithms 4th ed., Sedgewick Algorithms 4ed, NetworkX docs, PyG 2.5).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — graph algorithms + GNN 응용 정리