smx.graph.centrality

LRC (Local Reaching Centrality) computation and cross-seed aggregation.

Functions

compute_lrc(→ pandas.DataFrame)	Compute Local Reaching Centrality (LRC) for every node of graph.
aggregate_lrc_across_seeds(→ Tuple[pandas.DataFrame, ...)	Aggregate per-seed LRC DataFrames into a mean-aggregated ranking.

Module Contents

smx.graph.centrality.compute_lrc(graph: networkx.DiGraph, predicates_df: pandas.DataFrame) → pandas.DataFrame

Compute Local Reaching Centrality (LRC) for every node of graph.

LRC measures how well a node can reach other nodes in the graph, weighted by edge weights. Higher LRC → more central / important.

Parameters

graphnx.DiGraph

Directed predicate graph (e.g., from smx.graph.builder.PredicateGraphBuilder).

predicates_dfpd.DataFrame

Predicate catalogue with columns 'rule', 'zone', 'thresholds', 'operator'.

Returns

pd.DataFrame

Columns: Node, Local_Reaching_Centrality, Zone, Threshold, Operator. Sorted descending by LRC.

smx.graph.centrality.aggregate_lrc_across_seeds(lrc_by_seed: Dict[int, pandas.DataFrame], random_seeds: List[int]) → Tuple[pandas.DataFrame, pandas.DataFrame]

Aggregate per-seed LRC DataFrames into a mean-aggregated ranking.

Parameters

lrc_by_seeddict

{seed: lrc_df} where each lrc_df is returned by compute_lrc() (must have column 'Node' plus 'Local_Reaching_Centrality', 'Zone', 'Threshold', 'Operator').

random_seedslist of int

Seeds to include in the aggregation (keys of lrc_by_seed).

Returns

lrc_summed_dfpd.DataFrame

Mean-aggregated LRC for all predicates, sorted descending.

lrc_summed_unique_dfpd.DataFrame

Zone-deduplicated version of lrc_summed_df (one row per zone), keeping the highest-ranked predicate per zone.