population op• 데이터 종류: matrix × matrix → table
• 호출: import fullseye as fs; fs.ledger.graph_strength_growth(a, b, hub_fraction=0.1)(구현을 직접 호출하려면 import graphinv; graphinv.graph_strength_growth(a, b, hub_fraction=0.1), 원장에서 가져오려면 opsgraph.get("graph_strength_growth"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
Where the new synapses went — one wiring matrix `a (earlier) against b` (later).
Both are weight matrices (synapse counts) on the same node order; self-loops are
dropped. With `S = sum of each matrix and dS = S_b - S_a`:
• per node: `in_a, out_a, in_b, out_b (strengths), degree_a` (number of
partners, in + out, presence `> 0), gain_in = in_b - in_a, gain_out`.
Identity: `gain_in.sum() == gain_out.sum() == dS` (every synapse has one pre and one post).
• `strengthened / weakened (weight change on edges present in both), added`
(weight of edges only in `b), lost (weight of edges only in a`);
`strengthened - weakened + added - lost == dS`.
• `rho_in / rho_out — Spearman rank correlation of degree_a with gain_in` /
`gain_out over nodes with degree_a > 0` (0.0 with fewer than 3 such nodes or no
variance); `n_ranked`.
• hubs = the top `hub_fraction of ranked nodes by degree_a` (at least 1):
`hub_share_in_a (their share of S_a as post-synaptic partners), hub_share_gain_in`
(their share of `dS` arriving as inputs), and the same for outputs. If new synapses were
spread in proportion to existing strength, the two shares would be equal — the gap is
what "hubs grow disproportionately" means in numbers.
Raises `ValueError`: either matrix not square / non-finite / negative / a string or
masked array; shapes differ; `hub_fraction outside (0, 1]; dS == 0` is allowed
(shares of the gain are then 0).
• 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_synapses_vs_neurites — py -3.11 examples/poc_worm_synapses_vs_neurites.py
table 를 입력으로 받는 것)graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
population)graph_edge_consensus · graph_core_persistence
*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.