A PyTorch implementation of the RolX (Role eXtraction) algorithm for role discovery in graphs. This package provides efficient, GPU-accelerated tools for extracting structural roles from network data using feature-based approaches.
RolX is an unsupervised learning approach that discovers roles in networks based on recursive feature extraction and non-negative matrix factorization. This implementation provides:
- ReFeX: Recursive Feature Extraction to generate node features based on local and neighborhood properties
- RolX: Role extraction using Non-negative Matrix Factorization (NMF) on the extracted features
- GPU Acceleration: Leverages PyTorch for efficient computation on both CPU and GPU
pip install torch-rolx- Python 3.6+
- PyTorch
- NetworkX
- NumPy
import networkx as nx
from torch_rolx.rolx import RolX
# Create a sample graph
G = nx.karate_club_graph()
# Initialize RolX with 4 roles
rolx = RolX(n_roles=4, device='cpu') # Use 'cuda' for GPU acceleration
# Extract roles
role_assignments = rolx.fit_transform(G)
# Print role assignments for the first 5 nodes
print(role_assignments[:5])
# Get role features
role_features = rolx.get_role_features()
print(role_features)from torch_rolx.rolx import RolX
# Create RolX with custom parameters
rolx = RolX(
n_roles=5, # Number of roles to extract
max_iterations=3, # Maximum iterations for feature extraction
n_epochs=2000, # Training epochs for NMF
learning_rate=0.005, # Learning rate for optimizer
device='cuda' # Use GPU if available
)
# The rest of your code...The main class for role extraction.
rolx = RolX(
n_roles=4, # Number of roles to discover
max_iterations=4, # Maximum iterations for ReFeX
refex_params=None, # Additional parameters for ReFeX
n_epochs=1000, # Number of epochs for NMF training
learning_rate=0.01, # Learning rate
device='cpu' # Computation device ('cpu' or 'cuda')
)fit(G): Train the RolX model on graph Gtransform(): Get role assignments for nodesfit_transform(G): Train the model and return node role assignmentsget_role_features(): Get feature vectors for each role
Extracts recursive features from graphs.
refex = ReFeX(
max_iterations=2, # Maximum number of recursive iterations
normalize=True, # Whether to normalize features
device='cpu' # Computation device
)-
Henderson, K., Gallagher, B., Eliassi-Rad, T., Tong, H., Basu, S., Akoglu, L., Koutra, D., Faloutsos, C., & Li, L. (2012). RolX: Structural Role Extraction & Mining in Large Graphs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
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Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., & Faloutsos, C. (2011). It's Who You Know: Graph Mining Using Recursive Structural Features. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.