graph_strength_growth — GRAPH population op

• 資料種類:matrix × matrix → table

• 呼叫: import fullseye as fs; fs.ledger.graph_strength_growth(a, b, hub_fraction=0.1)(要直接呼叫實作,import graphinv; graphinv.graph_strength_growth(a, b, hub_fraction=0.1);從台帳取用則 opsgraph.get("graph_strength_growth"))

用法

> 該運算子的說明尚無譯文,以下照原文給出。

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).

參考(範例資料・文獻)

• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。

• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。

• 演算法的正典(作者・年份)與用途見上面的族使用指南。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• poc_worm_synapses_vs_neurites — py -3.11 examples/poc_worm_synapses_vs_neurites.py

型別可銜接的下一個運算子(可接受 table 作為輸入)

graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length

同類別(population)

graph_edge_consensus · graph_core_persistence


*Provenance: graphinv.py — GRAPH 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.