datasynth_graph/ml/
mod.rs

1//! Machine learning utilities for graph neural networks.
2//!
3//! This module provides comprehensive feature engineering for graph-based ML:
4//! - Structural features (degree, clustering, etc.)
5//! - Temporal sequence features (velocity, burst, trend)
6//! - Motif detection (cycles, stars, back-and-forth)
7//! - Relationship features (counterparty concentration, risk)
8//! - Entity group detection and aggregation
9
10pub mod aggregation;
11pub mod entity_groups;
12mod features;
13pub mod motifs;
14pub mod relationship_features;
15mod splits;
16pub mod temporal;
17
18pub use aggregation::{
19    aggregate_all_groups, aggregate_features, aggregate_node_features, aggregate_values,
20    aggregate_weighted, AggregatedFeatures, AggregationType, MultiFeatureAggregation,
21};
22pub use entity_groups::{
23    detect_entity_groups, EntityGroup, GroupDetectionAlgorithm, GroupDetectionConfig,
24    GroupDetectionResult, GroupType,
25};
26pub use features::*;
27pub use motifs::{
28    compute_motif_features, detect_motifs, find_back_and_forth, find_circular_flows,
29    find_star_patterns, CircularFlow, GraphMotif, MotifConfig, MotifDetectionResult, MotifInstance,
30};
31pub use relationship_features::{
32    compute_all_combined_features, compute_all_counterparty_risk,
33    compute_all_relationship_features, compute_counterparty_risk, compute_relationship_features,
34    CombinedRelationshipFeatures, CounterpartyRisk, RelationshipFeatureConfig,
35    RelationshipFeatures,
36};
37pub use splits::*;
38pub use temporal::{
39    compute_all_temporal_features, compute_temporal_sequence_features, TemporalConfig,
40    TemporalFeatures, TemporalIndex, WindowFeatures,
41};
datasynth_graph/ml/mod.rs

datasynth_graph/ml/
mod.rs
