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Basic tutorial:

  • Core idea and principles
  • py3plex - key principles
  • Analysis of multilayers
  • Analysis of multiplex networks
  • Supra adjacency matrices
  • Network visualization
  • Community detection (multiplex)
  • Supra adjacency matrices
  • Random networks

Further steps: learning:

  • Learning - label propagation
  • Learning - Node embeddings

API documentation:

  • py3plex
    • py3plex package
      • Subpackages
      • Module contents
py3plex
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  • py3plex »
  • py3plex package »
  • py3plex.algorithms package »
  • py3plex.algorithms.hedwig package
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py3plex.algorithms.hedwig package¶

Subpackages¶

  • py3plex.algorithms.hedwig.core package
    • Submodules
    • py3plex.algorithms.hedwig.core.converters module
    • py3plex.algorithms.hedwig.core.example module
    • py3plex.algorithms.hedwig.core.helpers module
    • py3plex.algorithms.hedwig.core.kb module
    • py3plex.algorithms.hedwig.core.load module
    • py3plex.algorithms.hedwig.core.predicate module
    • py3plex.algorithms.hedwig.core.rule module
    • py3plex.algorithms.hedwig.core.settings module
    • py3plex.algorithms.hedwig.core.term_parsers module
    • Module contents
  • py3plex.algorithms.hedwig.learners package
    • Submodules
    • py3plex.algorithms.hedwig.learners.bottomup module
    • py3plex.algorithms.hedwig.learners.learner module
    • py3plex.algorithms.hedwig.learners.optimal module
    • Module contents
  • py3plex.algorithms.hedwig.stats package
    • Submodules
    • py3plex.algorithms.hedwig.stats.adjustment module
    • py3plex.algorithms.hedwig.stats.scorefunctions module
    • py3plex.algorithms.hedwig.stats.significance module
    • py3plex.algorithms.hedwig.stats.validate module
    • Module contents

Module contents¶

py3plex.algorithms.hedwig.build_graph(kwargs)¶
py3plex.algorithms.hedwig.generate_rules_report(kwargs, rules_per_target, human=<function <lambda>>)¶
py3plex.algorithms.hedwig.rule_kernel(target)¶
py3plex.algorithms.hedwig.run(kwargs, cli=True, generator_tag=False, num_threads='all')¶
py3plex.algorithms.hedwig.run_learner(kwargs, kb, validator, generator=False, num_threads='all')¶
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