graph_strength_growth — GRAPH population op

• 数据种类:matrix × matrix → table

• 调用: import fullseye as fs; fs.ledger.graph_strength_growth(a, b, hub_fraction=0.1)(要直接调用实现,import graphinv; graphinv.graph_strength_growth(a, b, hub_fraction=0.1);从台账取用则 opsgraph.get("graph_strength_growth"))

用法

> 该算子的说明尚无译文,以下照原文给出。

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).

参考(示例数据・文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者・年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• poc_worm_synapses_vs_neurites — py -3.11 examples/poc_worm_synapses_vs_neurites.py

类型可衔接的下一个算子(可接受 table 作为输入)

graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length

同类别(population)

graph_edge_consensus · graph_core_persistence


*Provenance: graphinv.py — GRAPH 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

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