Crate scirs2_graph 

SciRS2 Graph - Graph Algorithms and Network Analysis

scirs2-graph provides comprehensive graph algorithms and data structures for network analysis, offering shortest paths, centrality measures, community detection, spectral methods, and graph embeddings with parallel processing and memory-efficient implementations.

🎯 Key Features

• Graph Representations: Directed, undirected, weighted, multi-graphs
• Classic Algorithms: BFS, DFS, Dijkstra, A*, Bellman-Ford, Floyd-Warshall
• Centrality Measures: Betweenness, closeness, eigenvector, PageRank
• Community Detection: Louvain, label propagation, spectral clustering
• Spectral Methods: Graph Laplacian, spectral clustering, graph embeddings
• Network Metrics: Clustering coefficient, diameter, average path length
• Performance: Parallel algorithms, memory profiling

📦 Module Overview

SciRS2 Module	NetworkX/igraph Equivalent	Description
algorithms	networkx.algorithms	Core graph algorithms (BFS, DFS, shortest paths)
measures	networkx.centrality	Centrality and network metrics
spectral	scipy.sparse.linalg	Spectral graph theory and embeddings
generators	networkx.generators	Graph generation (random, regular, etc.)
io	networkx.readwrite	Graph I/O (GML, GraphML, edge lists)

🚀 Quick Start

[dependencies]
scirs2-graph = "0.4.3"

use scirs2_graph::{Graph, breadth_first_search, betweenness_centrality};

// Create graph and run BFS
let mut g: Graph<i32, f64> = Graph::new();
let n0 = g.add_node(0);
let n1 = g.add_node(1);
g.add_edge(0, 1, 1.0);

🔒 Version: 0.4.3

API Stability and Versioning

scirs2-graph follows strict semantic versioning with clear stability guarantees:

Stability Classifications

• ✅ Stable: Core APIs guaranteed until next major version (2.0.0)
• ⚠️ Experimental: May change in minor versions, marked with #[cfg(feature = "experimental")]
• 📋 Deprecated: Will be removed in next major version, use alternatives

Version Guarantees

• MAJOR (1.x.x → 2.x.x): Breaking changes to stable APIs allowed
• MINOR (1.0.x → 1.1.x): New features, deprecations only (no breaks to stable APIs)
• PATCH (1.0.0 → 1.0.1): Bug fixes only, no API changes

Stable Core APIs (v0.4.0+)

• Graph data structures (Graph, DiGraph, MultiGraph)
• Basic algorithms (traversal, shortest paths, connectivity)
• Graph generators and I/O operations
• Community detection with _result suffix functions
• Error handling and core types

Re-exports

pub use algorithms::articulation_points;

pub use algorithms::astar_search;

pub use algorithms::astar_search_digraph;

pub use algorithms::betweenness_centrality;

pub use algorithms::bidirectional_search;

pub use algorithms::bidirectional_search_digraph;

pub use algorithms::breadth_first_search;

pub use algorithms::breadth_first_search_digraph;

pub use algorithms::bridges;

pub use algorithms::capacity_scaling_max_flow;

pub use algorithms::center_nodes;

pub use algorithms::closeness_centrality;

pub use algorithms::complement;

pub use algorithms::connected_components;

pub use algorithms::cosine_similarity;

pub use algorithms::depth_first_search;

pub use algorithms::depth_first_search_digraph;

pub use algorithms::diameter;

pub use algorithms::dijkstra_path;

pub use algorithms::dinic_max_flow;

pub use algorithms::dinic_max_flow_full;

pub use algorithms::edge_subgraph;

pub use algorithms::edmonds_karp_max_flow;

pub use algorithms::eigenvector_centrality;

pub use algorithms::eulerian_type;

pub use algorithms::floyd_warshall;

pub use algorithms::floyd_warshall_digraph;

pub use algorithms::fluid_communities_result;

pub use algorithms::ford_fulkerson_max_flow;

pub use algorithms::girvan_newman_communities_result;

pub use algorithms::girvan_newman_result;

pub use algorithms::greedy_coloring;

pub use algorithms::greedy_modularity_optimization_result;

pub use algorithms::hierarchical_communities_result;

pub use algorithms::hopcroft_karp;

pub use algorithms::infomap_communities;

pub use algorithms::is_bipartite;

pub use algorithms::isap_max_flow;

pub use algorithms::jaccard_similarity;

pub use algorithms::k_core_decomposition;

pub use algorithms::k_shortest_paths;

pub use algorithms::label_propagation_result;

pub use algorithms::line_digraph;

pub use algorithms::line_graph;

pub use algorithms::louvain_communities_result;

pub use algorithms::maximal_matching;

pub use algorithms::maximum_bipartite_matching;

pub use algorithms::maximum_cardinality_matching;

pub use algorithms::min_cost_max_flow;

pub use algorithms::min_cost_max_flow_graph;

pub use algorithms::minimum_cut;

pub use algorithms::minimum_spanning_tree;

pub use algorithms::minimum_st_cut;

pub use algorithms::minimum_weight_bipartite_matching;

pub use algorithms::modularity;

pub use algorithms::modularity;

pub use algorithms::modularity_optimization_result;

pub use algorithms::multi_commodity_flow;

pub use algorithms::multi_source_multi_sink_max_flow;

pub use algorithms::pagerank;

pub use algorithms::parallel_max_flow;

pub use algorithms::personalized_pagerank;

pub use algorithms::push_relabel_max_flow;

pub use algorithms::push_relabel_max_flow_full;

pub use algorithms::radius;

pub use algorithms::random_walk;

pub use algorithms::random_walk;

pub use algorithms::stable_marriage;

pub use algorithms::strongly_connected_components;

pub use algorithms::subdigraph;

pub use algorithms::subgraph;

pub use algorithms::tensor_product;

pub use algorithms::topological_sort;

pub use algorithms::transition_matrix;

pub use algorithms::weight_filtered_subgraph;

pub use algorithms::AStarResult;

pub use algorithms::BipartiteMatching;

pub use algorithms::BipartiteResult;

pub use algorithms::CommunityResult;

pub use algorithms::CommunityStructure;

pub use algorithms::CostEdge;

pub use algorithms::DendrogramLevel;

pub use algorithms::EulerianType;

pub use algorithms::GirvanNewmanConfig;

pub use algorithms::GirvanNewmanResult;

pub use algorithms::GraphColoring;

pub use algorithms::HopcroftKarpResult;

pub use algorithms::InfomapResult;

pub use algorithms::MaxFlowResult;

pub use algorithms::MaximumMatching;

pub use algorithms::MinCostFlowResult;

pub use algorithms::MotifType;

pub use algorithms::MultiCommodityFlowResult;

pub use algorithms::parallel_label_propagation_result;

pub use algorithms::parallel_louvain_communities_result;

pub use algorithms::parallel_modularity;

pub use spectral::parallel_laplacian;

pub use spectral::parallel_spectral_clustering;

pub use algorithms::are_graphs_isomorphic;

pub use algorithms::are_graphs_isomorphic_enhanced;

pub use algorithms::cartesian_product;

pub use algorithms::chromatic_number;

pub use algorithms::find_isomorphism;

pub use algorithms::find_isomorphism_vf2;

pub use algorithms::find_motifs;

pub use algorithms::find_subgraph_matches;

pub use algorithms::graph_edit_distance;

pub use algorithms::has_hamiltonian_circuit;

pub use algorithms::has_hamiltonian_path;

pub use algorithms::coloring::chromatic_bounds;

pub use algorithms::coloring::dsatur_coloring;

pub use algorithms::coloring::edge_coloring;

pub use algorithms::coloring::greedy_coloring_with_order;

pub use algorithms::coloring::list_coloring;

pub use algorithms::coloring::verify_coloring;

pub use algorithms::coloring::welsh_powell;

pub use algorithms::coloring::ChromaticBounds;

pub use algorithms::coloring::ColoringOrder;

pub use algorithms::coloring::EdgeColoring;

pub use algorithms::coloring::ListColoring;

pub use algorithms::motifs::count_3node_motifs;

pub use algorithms::motifs::count_4node_motifs;

pub use algorithms::motifs::count_motif_frequencies;

pub use algorithms::motifs::frequent_subgraph_mining;

pub use algorithms::motifs::graphlet_degree_distribution;

pub use algorithms::motifs::sample_motif_frequencies;

pub use algorithms::motifs::vf2_subgraph_isomorphism;

pub use algorithms::motifs::wl_subtree_kernel;

pub use algorithms::motifs::FrequentPattern;

pub use algorithms::motifs::GraphletDDResult;

pub use algorithms::motifs::GraphletDegreeVector;

pub use algorithms::motifs::VF2Result;

pub use algorithms::motifs::WLKernelResult;

pub use link_prediction::adamic_adar_all;

pub use link_prediction::adamic_adar_index;

pub use link_prediction::common_neighbors_all;

pub use link_prediction::common_neighbors_score;

pub use link_prediction::compute_auc;

pub use link_prediction::evaluate_link_prediction;

pub use link_prediction::jaccard_coefficient;

pub use link_prediction::jaccard_coefficient_all;

pub use link_prediction::katz_similarity;

pub use link_prediction::katz_similarity_all;

pub use link_prediction::preferential_attachment;

pub use link_prediction::preferential_attachment_all;

pub use link_prediction::resource_allocation_all;

pub use link_prediction::resource_allocation_index;

pub use link_prediction::simrank;

pub use link_prediction::simrank_score;

pub use link_prediction::LinkPredictionConfig;

pub use link_prediction::LinkPredictionEval;

pub use link_prediction::LinkScore;

pub use base::BipartiteGraph;

pub use base::DiGraph;

pub use base::Edge;

pub use base::EdgeWeight;

pub use base::Graph;

pub use base::Hyperedge;

pub use base::Hypergraph;

pub use base::MultiDiGraph;

pub use base::MultiGraph;

pub use base::Node;

pub use error::ErrorContext;

pub use error::GraphError;

pub use error::Result;

pub use generators::barabasi_albert_graph;

pub use generators::complete_graph;

pub use generators::cycle_graph;

pub use generators::erdos_renyi_graph;

pub use generators::grid_2d_graph;

pub use generators::grid_3d_graph;

pub use generators::hexagonal_lattice_graph;

pub use generators::path_graph;

pub use generators::planted_partition_model;

pub use generators::power_law_cluster_graph;

pub use generators::random_geometric_graph;

pub use generators::star_graph;

pub use generators::stochastic_block_model;

pub use generators::triangular_lattice_graph;

pub use generators::two_community_sbm;

pub use generators::watts_strogatz_graph;

pub use measures::centrality;

pub use measures::clustering_coefficient;

pub use measures::graph_density;

pub use measures::hits_algorithm;

pub use measures::katz_centrality;

pub use measures::katz_centrality_digraph;

pub use measures::pagerank_centrality;

pub use measures::pagerank_centrality_digraph;

pub use measures::CentralityType;

pub use measures::HitsScores;

pub use measures::parallel_pagerank_centrality;

pub use spectral::laplacian;

pub use spectral::normalized_cut;

pub use spectral::spectral_radius;

pub use weighted::MultiWeight;

pub use weighted::NormalizationMethod;

pub use weighted::WeightStatistics;

pub use weighted::WeightTransform;

pub use weighted::WeightedOps;

pub use attributes::AttributeSummary;

pub use attributes::AttributeValue;

pub use attributes::AttributeView;

pub use attributes::AttributedDiGraph;

pub use attributes::AttributedGraph;

pub use attributes::Attributes;

pub use memory::suggest_optimizations;

pub use memory::BitPackedGraph;

pub use memory::CSRGraph;

pub use memory::CompressedAdjacencyList;

pub use memory::FragmentationReport;

pub use memory::HybridGraph;

pub use memory::MemoryProfiler;

pub use memory::MemorySample;

pub use memory::MemoryStats;

pub use memory::OptimizationSuggestions;

pub use memory::OptimizedGraphBuilder;

pub use performance::LargeGraphIterator;

pub use performance::LargeGraphOps;

pub use performance::MemoryMetrics;

pub use performance::ParallelConfig;

pub use performance::PerformanceMonitor;

pub use performance::PerformanceReport;

pub use performance::StreamingGraphProcessor;

pub use embeddings::DeepWalk;

pub use embeddings::DeepWalkConfig;

pub use embeddings::DeepWalkMode;

pub use embeddings::Embedding;

pub use embeddings::EmbeddingModel;

pub use embeddings::LINEConfig;

pub use embeddings::LINEOrder;

pub use embeddings::Node2Vec;

pub use embeddings::Node2VecConfig;

pub use embeddings::RandomWalk;

pub use embeddings::RandomWalkGenerator;

pub use embeddings::SpectralEmbedding;

pub use embeddings::SpectralEmbeddingConfig;

pub use embeddings::SpectralLaplacianType;

pub use embeddings::LINE;

pub use layout::circular_layout;

pub use layout::hierarchical_layout;

pub use layout::spectral_layout;

pub use layout::spring_layout;

pub use layout::Position;

pub use temporal::count_temporal_triangles;

pub use temporal::evolutionary_clustering;

pub use temporal::temporal_betweenness;

pub use temporal::temporal_closeness;

pub use temporal::temporal_motif_count;

pub use temporal::temporal_pagerank;

pub use temporal::DynamicCommunity;

pub use temporal::TemporalEdge as StreamTemporalEdge;

pub use temporal::TemporalGraph as StreamTemporalGraph;

pub use temporal::TemporalMotifCounts;

pub use temporal_interval::temporal_betweenness_centrality;

pub use temporal_interval::temporal_reachability;

pub use temporal_interval::TemporalGraph;

pub use temporal_interval::TemporalPath;

pub use temporal_interval::TimeInstant;

pub use temporal_interval::TimeInterval;

pub use advanced::create_advanced_processor;

pub use advanced::execute_with_advanced;

pub use advanced::AdvancedConfig;

pub use advanced::AdvancedProcessor;

pub use advanced::AdvancedStats;

pub use advanced::AlgorithmMetrics;

pub use advanced::GPUAccelerationContext;

pub use advanced::NeuralRLAgent;

pub use advanced::NeuromorphicProcessor;

pub use graph_memory_profiler::AdvancedMemoryProfiler;

pub use graph_memory_profiler::EfficiencyAnalysis;

pub use graph_memory_profiler::MemoryProfile;

pub use graph_memory_profiler::MemoryProfilerConfig;

pub use graph_memory_profiler::MemoryStats as GraphMemoryStats;

pub use graph_memory_profiler::OptimizationOpportunity;

pub use graph_memory_profiler::OptimizationType;

pub use numerical_accuracy_validation::create_comprehensive_validation_suite;

pub use numerical_accuracy_validation::run_quick_validation;

pub use numerical_accuracy_validation::AdvancedNumericalValidator;

pub use numerical_accuracy_validation::ValidationAlgorithm;

pub use numerical_accuracy_validation::ValidationConfig;

pub use numerical_accuracy_validation::ValidationReport;

pub use numerical_accuracy_validation::ValidationResult;

pub use numerical_accuracy_validation::ValidationTolerances;

pub use compressed::AdjacencyList;

pub use compressed::CsrGraph;

pub use compressed::CsrGraphBuilder;

pub use compressed::NeighborIter;

pub use parallel_algorithms::bfs;

pub use parallel_algorithms::connected_components as csr_connected_components;

pub use parallel_algorithms::pagerank as csr_pagerank;

pub use parallel_algorithms::triangle_count;

pub use parallel_algorithms::BfsResult;

pub use parallel_algorithms::ComponentsResult;

pub use parallel_algorithms::PageRankConfig;

pub use parallel_algorithms::PageRankResult;

pub use parallel_algorithms::TriangleCountResult;

pub use parallel_algorithms::parallel_bfs;

pub use parallel_algorithms::parallel_connected_components;

pub use parallel_algorithms::parallel_pagerank;

pub use parallel_algorithms::parallel_triangle_count;

pub use streaming::DegreeDistribution;

pub use streaming::DoulionTriangleCounter;

pub use streaming::EvictionStrategy;

pub use streaming::MascotTriangleCounter;

pub use streaming::MemoryBoundedConfig;

pub use streaming::MemoryBoundedProcessor;

pub use streaming::SlidingWindowGraph;

pub use streaming::StreamEdge;

pub use streaming::StreamEvent;

pub use streaming::StreamOp;

pub use streaming::StreamingGraph;

pub use streaming::TriangleCounterStats;

pub use io::*;

Modules

advanced

Advanced Mode Integration for Graph Processing

algebraic

Algebraic graph theory.

algorithms

Graph algorithms

alignment

Network alignment algorithms for finding correspondences between graphs.

attributed_graph

Attributed graphs with typed node and edge features

attributes

Node and edge attribute system

base

Base graph structures and operations

centrality

Extended centrality measures for graph analysis.

community

Community detection algorithms operating on weighted adjacency matrices.

compat

NetworkX Migration Utilities

compressed

Compressed Sparse Row (CSR) graph representation for large-scale graphs

condensation

Graph condensation (dataset distillation for graphs).

diffusion

Information diffusion models and influence maximization algorithms

distributed

Distributed graph storage with partitioned adjacency.

domination

Graph domination and covering problems.

dynamic

Dynamic graph analysis: temporal networks and streaming algorithms.

embeddings

Graph embedding algorithms and utilities

error

Error types for the graph processing module

flow

Standalone network flow algorithms (integer-capacity, index-based API).

generators

Graph generation algorithms

gnn

Graph Neural Network (GNN) layers and message-passing framework

gpu

GPU-accelerated and parallel graph algorithms.

graph_memory_profiler

Memory Usage Profiler for Advanced Mode

graph_transformer

Graph Transformer models: GraphGPS and Graphormer.

heterogeneous

Heterogeneous graphs with multiple node and edge types

hypergraph

Hypergraph algorithms and simplicial complexes.

io

Input/output operations for graphs

isomorphism

Graph isomorphism algorithms — WL hash and canonical labelling

knowledge_graph

Knowledge Graph Embedding models

layout

Graph layout algorithms for visualization

link_prediction

Link prediction algorithms for graph analysis

measures

Graph measures and metrics

memory

Memory profiling and optimization utilities for graph data structures

network_statistics

Network-level statistics and structural measures

numerical_accuracy_validation

Numerical Accuracy Validation for Advanced Mode

parallel_algorithms

Parallel graph algorithms for large-scale graph analytics

partitioning

Large-scale graph partitioning algorithms.

performance

Performance optimizations for large graph operations

planarity

Graph planarity testing and related algorithms.

reliability

Network reliability analysis.

sampling

Graph Sampling Algorithms

signal_processing

Graph Signal Processing (GSP) module.

signed_directed

Signed and directed graph learning with specialised embeddings.

simd_ops

SIMD-accelerated operations for graph algorithms

social

Social network analysis algorithms

spectral

Spectral graph theory operations

spectral_graph

Spectral graph theory — adjacency-matrix-based API.

ssl

Graph Self-Supervised Learning (SSL) methods.

streaming

Streaming graph processing — core module and advanced algorithms.

temporal

Temporal graph analysis algorithms (stream model, f64 timestamps)

temporal_graph

Temporal and Dynamic Graphs

temporal_interval

Temporal graph structures and algorithms

traversal

Enhanced graph traversal algorithms on adjacency matrices.

visualization

Graph visualization with layout algorithms and SVG export

weighted

Specialized weighted graph operations and analysis

Traits

IndexType

Trait for the unsigned integer type used for node and edge indices.