advanced

Advanced Topics

This directory contains examples for advanced multilayer network analysis techniques. These examples cover specialized topics like embeddings, dynamics, decomposition, and tensor operations.

Examples by Topic

Network Embeddings

Transform networks into vector representations for machine learning:

• example_embedding_construction.py - Construct Node2Vec embeddings
• example_embedding_visualization.py - Visualize network embeddings
• example_n2v_embedding.py - Node2Vec embedding examples

Network Dynamics and Processes

Simulate processes on networks:

• example_random_walks.py - Random walks on multilayer networks
• example_spreading.py - Simple spreading/diffusion processes
• example_sir_multiplex.py - SIR epidemic simulation on multiplex networks
• example_multiplex_dynamics.py - General dynamics on multiplex networks

Network Decomposition and Classification

Break down networks and classify nodes:

• example_decomposition_and_classification.py - Decompose networks for node classification
• example_network_decomposition.py - Network decomposition techniques
• example_network_decomposition_different_meta_paths.py - Decomposition with meta-paths
• example_decomposition_ground_truth.py - Decomposition with ground truth evaluation
• example_semantic_enrichment.py - Enrich networks with semantic information
• example_CBSSD.py - Cross-layer Betweenness-based Structural Similarity Decomposition
• example_PPR.py - Personalized PageRank examples

Advanced Network Operations

Specialized multilayer operations:

• example_inverse_network.py - Create inverse (transposed) networks
• example_layer_extraction.py - Extract and analyze individual layers
• example_manipulation.py - Advanced network manipulation techniques
• example_multiplex_aggregate.py - Aggregate multiplex networks
• example_multilayer_vectorized_aggregation.py - Vectorized aggregation (high performance)
• example_vectorized_aggregation.py - Vectorized multilayer operations

Tensor and Matrix Operations

Work with tensorial representations:

• example_tensorial_manipulation.py - Tensor-based network operations
• example_tensorial_manipulation_headless.py - Tensor operations (non-interactive)
• example_supra_adjacency.py - Supra-adjacency matrix operations
• example_incidence_gadget_encoding.py - Incidence gadget encoding for multiplex networks
• example_numeric_encoding.py - Numeric encoding schemes

Geometric Network Analysis

Apply differential geometry to networks:

• example_ricci_curvature.py - Ollivier-Ricci curvature and Ricci flow

When to Use These Examples

Use Embeddings When:

• You need feature representations for ML tasks
• You want to visualize high-dimensional networks
• You need to compute node similarities

Use Dynamics When:

• Modeling information/disease spread
• Studying cascading processes
• Analyzing temporal evolution

Use Decomposition When:

• Networks are heterogeneous
• You need to classify nodes
• You want to simplify complex networks

Use Tensor Operations When:

• Working with mathematical formulations
• Implementing custom algorithms
• Optimizing performance

Performance Notes

NOTE: Many examples in this category:

• May require significant computation time
• Might need external datasets
• Can be memory-intensive

Most are marked with SKIP_CI: slow or SKIP_CI: external_deps.

Related Examples

• Network Analysis - Basic metrics and centrality
• Communities - Community detection
• Workflows - Complete analysis pipelines