Temporal Clustering

TemporalClustering models communities that evolves as time goes by.

Each temporal community clustering observation is a Clustering object, thus it inherits all properties of its specific concrete class.

Overview

class TemporalClustering
add_clustering(clustering: object, time: object)

Add to the Temporal Clustering the communities observed at a given time

Parameters:
  • clustering – a Clustering object
  • time – time of observation
add_matching(matching: list)

Add a precomputed matching of the communities.

Parameters:matching – a list of tuples [(Ti_Ca, Tj_Cb, score), … ]. Community names needs to satisfy the pattern {tid}_{cid}, where tid is the time of observation and cid is the position of the community within the Clustering object.
clustering_stability_trend(method: Callable[[object, object], float]) → list

Returns the trend for community stability. The stability index is computed for temporally adjacent clustering pairs.

Parameters:method – a comparison score taking as input two Clustering objects (e.g., NMI, NF1, ARI…)
Returns:a list of floats
community_matching(method: Callable[[set, set], float], two_sided: bool = False) → list

Reconstruct community matches across adjacent observations using a provided similarity function.

Parameters:
  • method – a set similarity function with co-domain in [0,1] (e.g., Jaccard)
  • two_sided – boolean. Whether the match has to be applied only from the past to the future (False, default) or even from the future to the past (True)
Returns:

a list of tuples [(Ti_Ca, Tj_Cb, score), … ]. Community names are assigned following the pattern {tid}_{cid}, where tid is the time of observation and cid is the position of the community within the Clustering object.

get_clustering_at(time: object) → object

Returns the clustering observed at a given time

Parameters:time – the time of observation
Returns:a Clustering object
get_community(cid: str) → list

Returns the nodes within a given temporal community

Parameters:cid – community id of the form {tid}_{cid}, where tid is the time of observation and cid is the position of the community within the Clustering object.
Returns:list of nodes within cid
get_explicit_community_match() → list

Return an explicit matching of computed communities (if it exists)

Returns:a list of tuple [(Ti_Ca, Tj_Cb, score), … ]. Community names are assigned following the pattern {tid}_{cid}, where tid is the time of observation and cid is the position of the community within the Clustering object.
get_observation_ids() → list

Returns the list of temporal ids for the available clusterings :return: a list of temporal ids

has_explicit_match() → bool

Checks if the algorithm provided an explicit match of temporal communities

Returns:a list of tuple [(Ti_Ca, Tj_Cb, score), … ]. Community names are assigned following the pattern {tid}_{cid}, where tid is the time of observation and cid is the position of the community within the Clustering object.
lifecycle_polytree(method: Callable[[set, set], float] = None, two_sided: bool = False) → <Mock id='140619399614288'>

Reconstruct the poly-tree representing communities lifecycles using a provided similarity function.

Parameters:
  • method – a set similarity function with co-domain in [0,1] (e.g., Jaccard)
  • two_sided – boolean. Whether the match has to be applied only from the past to the future (False, default) or even from the future to the past (True)
Returns:

a networkx DiGraph object. Nodes represent communities, their ids are assigned following the pattern {tid}_{cid}, where tid is the time of observation and cid is the position of the community within the Clustering object.

to_json()

Generate a JSON representation of the TemporalClustering object

Returns:a JSON formatted string representing the object

Methods

Data transformation and IO

TemporalClustering.to_json() Generate a JSON representation of the TemporalClustering object
TemporalClustering.get_observation_ids() Returns the list of temporal ids for the available clusterings :return: a list of temporal ids
TemporalClustering.get_clustering_at(time) Returns the clustering observed at a given time
TemporalClustering.add_clustering(…) Add to the Temporal Clustering the communities observed at a given time
TemporalClustering.get_community(cid) Returns the nodes within a given temporal community

Evaluating Node Clustering

TemporalClustering.clustering_stability_trend(…) Returns the trend for community stability.

Matching temporal clustering

TemporalClustering.community_matching(…) Reconstruct community matches across adjacent observations using a provided similarity function.
TemporalClustering.lifecycle_polytree(…) Reconstruct the poly-tree representing communities lifecycles using a provided similarity function.