CDlib Logo
  • Overview
    • Who uses CDlib?
    • Goals
    • The Python CDlib library
    • Free software
    • EU H2020
  • Installing CDlib
    • Quick install
    • Optional Dependencies
      • Advanced
    • Installing from source
      • Source archive file
      • GitHub
    • Requirements
      • Python
  • Quick Start
    • Tutorial
    • FAQ
  • Reference
    • Community Objects
      • Which community should I use?
      • Community Types
    • Community Discovery algorithms
      • Static Community Discovery
      • Dynamic Community Discovery
      • Algorithms’ Table
    • Ensemble Methods
      • Configuration Objects
      • Multiple Instantiation
      • Optimal Configuration Search
    • Evaluation
      • Fitness Functions
      • Partition Comparisons
    • Benchmarks
      • Static Networks with Community Ground Truth
      • Dynamic Networks with Community Ground Truth
    • Input-Output
      • CSV format
      • JSON format
    • Visual Analytics
      • Network Visualization
      • Analytics plots
    • Remote Datasets
      • cdlib.datasets.available_networks
      • cdlib.datasets.available_ground_truths
      • cdlib.datasets.fetch_network_data
      • cdlib.datasets.fetch_ground_truth_data
      • cdlib.datasets.fetch_network_ground_truth
    • Utilities
      • Graph Transformation
      • Identifier mapping
  • Bibliography
    • Algorithms
    • Evaluation measures
    • Researches using CDlib
CDlib
  • Docs »
  • Reference »
  • Community Discovery algorithms »
  • Static Community Discovery »
  • cdlib.algorithms.hierarchical_link_community
  • Edit on GitHub

cdlib.algorithms.hierarchical_link_community¶

hierarchical_link_community(g_original: object) → cdlib.classes.edge_clustering.EdgeClustering¶

HLC (hierarchical link clustering) is a method to classify links into topologically related groups. The algorithm uses a similarity between links to build a dendrogram where each leaf is a link from the original network and branches represent link communities. At each level of the link dendrogram is calculated the partition density function, based on link density inside communities, to pick the best level to cut.

Supported Graph Types

Undirected Directed Weighted
Yes No No
Parameters:g_original – a networkx/igraph object
Returns:EdgeClustering object
Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> com = algorithms.hierarchical_link_community(G)
References:

Ahn, Yong-Yeol, James P. Bagrow, and Sune Lehmann. Link communities reveal multiscale complexity in networks. nature 466.7307 (2010): 761.

Next Previous

© Copyright 2019, Giulio Rossetti Revision 01c329a0.

Built with Sphinx using a theme provided by Read the Docs.