core op• Datenarten: matrix → table
• Aufruf: import fullseye as fs; fs.ledger.graph_kcore(adj, mode='total', weighted=False) (die Implementierung direkt: import graphinv; graphinv.graph_kcore(adj, mode='total', weighted=False); aus dem Register: opsgraph.get("graph_kcore"))
> Für diesen Operator gibt es noch keine Übersetzung. Es folgt der Originaltext unverändert.
k-core (or weighted s-core) decomposition of a wiring matrix — every node's core index.
A k-core is the maximal subgraph in which every node has degree at least k (Seidman 1983); a node's core index is the largest k of a core it belongs to. mode picks the degree: "in" (inputs a node receives), "out", "total" (in + out; a reciprocal pair counts twice, as in conngraph.graph_rich_club) or "undirected" (an edge in either direction counts once — this is what networkx.core_number computes on the symmetrised graph). With weighted=True the degree is the strength (sum of weights, e.g. synapse counts) and the result is the s-core of Eidsaa & Almaas 2013: the index is the running maximum of the minimum strength during peeling, so it is a real number with the units of the weights. Self-loops are dropped.
Returns a dict: core (n,) — the index per node (int for k-core, float for s-core); kmax — the innermost index; inner (n,) bool — nodes of the innermost core; n_inner; levels — the distinct indices in increasing order and counts — how many nodes have each (sum(counts) == n); mode; weighted.
Identities (the tests): a complete graph on n nodes has every index n−1; a tree 1; a cycle 2; undirected agrees with networkx.core_number on random graphs; the s-core of a 0/1 matrix equals the k-core; multiplying every weight by c multiplies every s-core index by c; the innermost core is never empty and every one of its nodes has at least kmax (weighted: strength) inside the core.
Raises ValueError: as graph_degree_summary (non-square, negative, non-finite, string / bool / complex / masked input); mode not one of in / out / total / undirected.
• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).
• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.
• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.
• poc_worm_core_persists — py -3.11 examples/poc_worm_core_persists.py
table als Eingabe)graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
core)*Provenance: graphinv.py — GRAPH Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.