core op• Data kinds: matrix → table
• Call: import fullseye as fs; fs.ledger.graph_kcore(adj, mode='total', weighted=False) (implementation को सीधे call करना हो तो import graphinv; graphinv.graph_kcore(adj, mode='total', weighted=False), registry से लेना हो तो opsgraph.get("graph_kcore"))
wiring matrix का k-core (या weighted s-core) decomposition — हर node का core index।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• poc_worm_core_persists — py -3.11 examples/poc_worm_core_persists.py
table को input के रूप में लेते हैं)graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
core)*Provenance: graphinv.py — GRAPH operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.