스킬 networkx
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그래프와 네트워크 데이터를 다루려면 구조 생성, 알고리즘 계산, 시각화 생성에 특화된 도구가 필요합니다. 이 스킬은 사회적 네트워크 분석, 생물학적 경로 매핑, 교통 최적화, 관계 모델링을 위한 NetworkX 기능에 대한 포괄적 접근을 제공합니다.
지원: Claude Codex Code(CC)
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설정 → 기능 → 스킬 → 스킬 업로드로 이동
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토글을 켜고 사용 시작
테스트해 보기
"networkx" 사용 중입니다. Create a graph showing connections between team members
예상 결과:
- 팀 구성원을 나타내는 8개의 노드로 그래프 생성
- 직속 보고 관계를 위한 에지 추가
- 팀 구성원 이름으로 노드 라벨 지정
- 가독성을 위해 원형 레이아웃으로 그리기
"networkx" 사용 중입니다. Find the most central nodes in a citation network
예상 결과:
- 50개 모든 노드에 대한 매개 중심성 계산
- 가장 중심적인 상위 5개 논문과 점수 식별
- 결과를 centrality_results.csv에 저장
- 시각화에서 중심성 값에 따라 노드 크기 표시
보안 감사
안전v4 • 1/17/2026
This is a documentation-only skill containing markdown files for the NetworkX Python library. All 495 static findings are FALSE POSITIVEs. The analyzer misinterprets markdown code fences as shell execution, NetworkX function names as cryptographic algorithms, and graph operations as system reconnaissance. No executable code exists. All URLs point to official NetworkX documentation.
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⚙️ 외부 명령어 (349)
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감사자: claude 감사 이력 보기 →
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만들 수 있는 것
사회적 네트워크 분석
소셜 미디어 데이터에서 관계 패턴을 분석하고, 영향력 있는 사용자를 식별하며, 커뮤니티를 탐지합니다
생물학적 경로 매핑
단백질 상호작용, 유전자 조절 네트워크, 대사 경로를 모델링합니다
교통 최적화
경로 계획, 공급망 네트워크, 인프라 설계를 최적화합니다
이 프롬프트를 사용해 보세요
기본 그래프 생성
Create a graph with 5 nodes and add edges between them to represent a basic network structure
경로 탐색
Find the shortest path between node A and node F in this graph and show the distance
중심성 분석
Compute betweenness centrality for all nodes and identify which nodes are most critical
네트워크 생성
Generate a Barabasi-Albert scale-free network with 100 nodes and 3 edges per new node
모범 사례
- 재현 가능한 랜덤 그래프 생성과 force-directed 레이아웃을 위해 랜덤 시드(seed=42)를 설정합니다
- 대규모 네트워크 작업 시 희소 행렬 형식과 적절한 데이터 구조를 사용합니다
- 노드 및 에지 속성을 보존하려면 그래프를 GraphML 형식으로 저장합니다
피하기
- 노드 식별자로 부동소수점 수를 사용하면 알고리즘에서 정밀도 문제가 발생할 수 있습니다
- 부분 그래프 분석만 필요할 때 전체 대규모 네트워크를 로드하면 메모리를 낭비합니다
- 연결 그래프가 필요한 알고리즘을 실행하기 전에 그래프 연결성을 확인하지 않음
자주 묻는 질문
NetworkX는 어떤 그래프 타입을 지원하나요?
NetworkX는 Graph(무방향), DiGraph(방향), MultiGraph(다중 에지 무방향), MultiDiGraph(다중 에지 방향)을 지원합니다.
CSV 파일에서 그래프를 어떻게 읽나요?
pandas로 CSV를 읽은 다음 source, target, 선택적 에지 속성 열을 사용하여 nx.from_pandas_edgelist()를 사용하세요.
최단 경로를 찾는 가장 빠른 방법은 무엇인가요?
단일 쌍에는 nx.shortest_path()를 사용하세요. 모든 쌍에는 메모리를 절약하기 위해 이터레이터와 함께 nx.all_pairs_shortest_path()를 사용하세요.
NetworkX는 방향 비순환 그래프를 처리할 수 있나요?
네, NetworkX는 nx.is_directed_acyclic_graph() 같은 함수와 nx.topological_sort()를 통한 위상 정렬로 DAG를 처리합니다.
네트워크에서 커뮤니티를 어떻게 탐지하나요?
모듈러리티 기반 탐지는 community.greedy_modularity_communities(G)를 사용하거나 networkx.algorithms.community의 다른 알고리즘을 사용하세요.
NetworkX와 함께 사용할 수 있는 시각화 라이브러리는 무엇인가요?
Matplotlib는 기본적으로 통합됩니다. 대화형 시각화에는 NetworkX 그래프 객체와 함께 PyVis 또는 Plotly를 사용하세요.