core op• 데이터 종류: matrix → table
• 호출: import fullseye as fs; fs.ledger.graph_kcore(adj, mode='total', weighted=False)(구현을 직접 호출하려면 import graphinv; graphinv.graph_kcore(adj, mode='total', weighted=False), 원장에서 가져오려면 opsgraph.get("graph_kcore"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
k-core (or weighted s-core) decomposition of a wiring matrix — every node's core index.
A k-core is the maximal subgraph in which every node has degree at least k (Seidman 1983); a node's core index is the largest k of a core it belongs to. mode picks the degree: "in" (inputs a node receives), "out", "total" (in + out; a reciprocal pair counts twice, as in conngraph.graph_rich_club) or "undirected" (an edge in either direction counts once — this is what networkx.core_number computes on the symmetrised graph). With weighted=True the degree is the strength (sum of weights, e.g. synapse counts) and the result is the s-core of Eidsaa & Almaas 2013: the index is the running maximum of the minimum strength during peeling, so it is a real number with the units of the weights. Self-loops are dropped.
Returns a dict: core (n,) — the index per node (int for k-core, float for s-core); kmax — the innermost index; inner (n,) bool — nodes of the innermost core; n_inner; levels — the distinct indices in increasing order and counts — how many nodes have each (sum(counts) == n); mode; weighted.
Identities (the tests): a complete graph on n nodes has every index n−1; a tree 1; a cycle 2; undirected agrees with networkx.core_number on random graphs; the s-core of a 0/1 matrix equals the k-core; multiplying every weight by c multiplies every s-core index by c; the innermost core is never empty and every one of its nodes has at least kmax (weighted: strength) inside the core.
Raises ValueError: as graph_degree_summary (non-square, negative, non-finite, string / bool / complex / masked input); mode not one of in / out / total / undirected.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• poc_worm_core_persists — py -3.11 examples/poc_worm_core_persists.py
table 를 입력으로 받는 것)graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
core)*Provenance: graphinv.py — GRAPH 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.