population op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.graph_core_persistence(adjs, mode='in', weighted=False) (to call the implementation directly, import graphinv; graphinv.graph_core_persistence(adjs, mode='in', weighted=False); from the registry, opsgraph.get("graph_core_persistence"))
Which nodes sit in the innermost core of every individual — K wirings on one node order.
`adjs is a list of K square matrices or a table {name: matrix}` on the same
node order (a node absent from an individual is a zero row and column). For each
individual the innermost core of :func:graph_kcore (same `mode / weighted`) is
taken; `appearances[v]` counts the individuals whose innermost core contains v.
Following Yadav & Singh 2026, a node is persistent if it is in the core of all K,
recurrent if in 2 .. K−1, transient if in exactly 1, and never otherwise.
Returns a dict: `k (K), n, names, membership` (K, n) bool,
`appearances (n,), persistent / recurrent / transient / never` (n,)
bool masks, `n_persistent .. n_never, kmax` (K,) the innermost index per
individual, `n_inner (K,), mode, weighted`.
Identities: `sum(n_inner) == sum(appearances)`; the four classes partition the
nodes; K identical inputs make every core node persistent and nothing recurrent or
transient; every persistent node is in every individual's innermost core.
Raises `ValueError: adjs` not a list / table; fewer than 2 individuals;
matrices of different sizes; any refusal of :func:graph_kcore.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_worm_core_persists — py -3.11 examples/poc_worm_core_persists.py
table as input)graph_edge_consensus · tree_morphometry · tree_sholl · tree_run_length
population)graph_edge_consensus · graph_strength_growth
*Provenance: graphinv.py — GRAPH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.