List of all classes, functions and methods in python-igraph

module documentation

Classes related to graph clustering.

Variable | `__license__` |
Undocumented |

Class | `Clustering` |
Class representing a clustering of an arbitrary ordered set. |

Class | `VertexClustering` |
The clustering of the vertex set of a graph. |

Class | `Dendrogram` |
The hierarchical clustering (dendrogram) of some dataset. |

Class | `VertexDendrogram` |
The dendrogram resulting from the hierarchical clustering of the vertex set of a graph. |

Class | `Cover` |
Class representing a cover of an arbitrary ordered set. |

Class | `VertexCover` |
The cover of the vertex set of a graph. |

Class | `CohesiveBlocks` |
The cohesive block structure of a graph. |

Function | `compare_communities` |
Compares two community structures using various distance measures. |

Function | `split_join_distance` |
Calculates the split-join distance between two community structures. |

Function | `_handle_mark_groups_arg_for_clustering` |
Handles the mark_groups=... keyword argument in plotting methods of clusterings. |

Function | `_prepare_community_comparison` |
Auxiliary method that takes two community structures either as membership lists or instances of `Clustering` , and returns a tuple whose two elements are membership lists. |

def _handle_mark_groups_arg_for_clustering(mark_groups, clustering):

Handles the mark_groups=... keyword argument in plotting methods of clusterings.

This is an internal method, you shouldn't need to mess around with it. Its purpose is to handle the extended semantics of the mark_groups=... keyword argument in the `__plot__`

method of `VertexClustering`

and `VertexCover`

instances, namely the feature that numeric IDs are resolved to clusters automatically.

def _prepare_community_comparison(comm1, comm2, remove_none=False):

Auxiliary method that takes two community structures either as membership lists or instances of `Clustering`

, and returns a tuple whose two elements are membership lists.

This is used by `compare_communities`

and `split_join_distance`

.

Parameters | comm1 | the first community structure as a membership list or as a `Clustering` object. |

comm2 | the second community structure as a membership list or as a `Clustering` object. | |

remove_none | whether to remove `None` entries from the membership lists. If `remove_none` is `False` , a `None` entry in either `comm1` or `comm2` will result in an exception. If `remove_none` is `True` , `None` values are filtered away and only the remaining lists are compared. |

def compare_communities(comm1, comm2, method='vi', remove_none=False):

Compares two community structures using various distance measures.

Parameters | comm1 | the first community structure as a membership list or as a `Clustering` object. |

comm2 | the second community structure as a membership list or as a `Clustering` object. | |

method | the measure to use. `"vi"` or `"meila"` means the variation of information metric of Meila (2003), `"nmi"` or `"danon"` means the normalized mutual information as defined by Danon et al (2005), `"split-join"` means the split-join distance of van Dongen (2000), `"rand"` means the Rand index of Rand (1971), `"adjusted_rand"` means the adjusted Rand index of Hubert and Arabie (1985). | |

remove_none | whether to remove `None` entries from the membership lists. This is handy if your `Clustering` object was constructed using `VertexClustering.FromAttribute` using an attribute which was not defined for all the vertices. If `remove_none` is `False` , a `None` entry in either `comm1` or `comm2` will result in an exception. If `remove_none` is `True` , `None` values are filtered away and only the remaining lists are compared. | |

Returns | the calculated measure. | |

Unknown Field: newfield | ref | Reference |

Unknown Field: ref | Meila M: Comparing clusterings by the variation of information. In: Scholkopf B, Warmuth MK (eds). Learning Theory and Kernel Machines: 16th Annual Conference on Computational Learning Theory and 7th Kernel Workship, COLT/Kernel 2003, Washington, DC, USA. Lecture Notes in Computer Science, vol. 2777, Springer, 2003. ISBN: 978-3-540-40720-1. | |

Danon L, Diaz-Guilera A, Duch J, Arenas A: Comparing community structure identification. J Stat Mech P09008, 2005. | ||

van Dongen D: Performance criteria for graph clustering and Markov cluster experiments. Technical Report INS-R0012, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000. | ||

Rand WM: Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846-850, 1971. | ||

Hubert L and Arabie P: Comparing partitions. Journal of Classification 2:193-218, 1985. |

def split_join_distance(comm1, comm2, remove_none=False):

Calculates the split-join distance between two community structures.

The split-join distance is a distance measure defined on the space of partitions of a given set. It is the sum of the projection distance of one partition from the other and vice versa, where the projection number of A from B is if calculated as follows:

- For each set in A, find the set in B with which it has the maximal overlap, and take note of the size of the overlap.
- Take the sum of the maximal overlap sizes for each set in A.
- Subtract the sum from
*n*, the number of elements in the partition.

Note that the projection distance is asymmetric, that's why it has to be calculated in both directions and then added together. This function returns the projection distance of `comm1`

from `comm2`

and the projection distance of `comm2`

from `comm1`

, and returns them in a pair. The actual split-join distance is the sum of the two distances. The reason why it is presented this way is that one of the elements being zero then implies that one of the partitions is a subpartition of the other (and if it is close to zero, then one of the partitions is close to being a subpartition of the other).

Parameters | comm1 | the first community structure as a membership list or as a `Clustering` object. |

comm2 | the second community structure as a membership list or as a `Clustering` object. | |

remove_none | whether to remove `None` entries from the membership lists. This is handy if your `Clustering` object was constructed using `VertexClustering.FromAttribute` using an attribute which was not defined for all the vertices. If `remove_none` is `False` , a `None` entry in either `comm1` or `comm2` will result in an exception. If `remove_none` is `True` , `None` values are filtered away and only the remaining lists are compared. | |

Returns | the projection distance of `comm1` from `comm2` and vice versa in a tuple. The split-join distance is the sum of the two. | |

See Also | `compare_communities()` with `method = "split-join"` if you are not interested in the individual projection distances but only the sum of them. | |

Unknown Field: newfield | ref | Reference |

Unknown Field: ref | van Dongen D: Performance criteria for graph clustering and Markov cluster experiments. Technical Report INS-R0012, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000. |