core op• 데이터 종류: matrix → table
• 호출: import fullseye as fs; fs.ledger.graph_rich_club_curve(adj, mode='total', n_null=20, swaps_per_edge=5, seed=0)(구현을 직접 호출하려면 import graphinv; graphinv.graph_rich_club_curve(adj, mode='total', n_null=20, swaps_per_edge=5, seed=0), 원장에서 가져오려면 opsgraph.get("graph_rich_club_curve"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
Rich-club coefficient φ(k) for every k, against the degree-preserving null.
φ(k) is the directed density of the subgraph spanned by the nodes whose degree exceeds
k: `edges among them / (r (r − 1)), r` = their number (Colizza et al. 2006; the
same formula as :func:conngraph.graph_rich_club, here for all k at once). `mode`
is the degree used to rank nodes: `"total" (in + out), "in" or "out"` (Yadav
& Singh 2026 rank by in- and out-degree separately). A rising φ(k) alone means little —
high-degree nodes are dense in any graph — so φ is divided by its mean over `n_null`
rewirings that keep every node's in- and out-degree exactly (Maslov & Sneppen 2002;
every sample is checked, fail-closed). `ratio > 1` is the rich-club regime; Towlson
et al. 2013 call it significant where `ratio > 1 + sd`.
Returns a dict: `k (all integers 0 .. max degree − 1), count` (nodes with degree
> k), `phi, null_mean, null_sd, ratio` (nan where the null mean is 0),
`z, regime (bool: ratio > 1 + null sd of the ratio), n_null, mode`.
Identities: `phi[k] == conngraph.graph_rich_club(adj, k)` for every k with
`mode="total"; a complete graph has phi == 1 and ratio == 1` everywhere
(no swap can change it); a rewired sample of the graph itself has ratio ≈ 1.
Raises `ValueError: as :func:graph_degree_preserving_null` (non-square,
negative, ... ; `n_null < 2; swaps_per_edge < 1`; fewer than 2 edges);
`mode` not in / out / total.
• 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.