Event Analytics¶

This module contains some

Events¶

lifecycles provides a quick way to compute statistics related to group evolution. The functions below are used to compute event facet scores as defined in the paper.

`lifecycles.algorithms.analyze_all_flows`	Analyze the flow of all sets in a LifeCycle object w.r.t.
`lifecycles.algorithms.analyze_flow`	Analyze the flow of a set with respect to a given temporal direction.
`lifecycles.algorithms.event`	Compute the event type and typicality of a target set in a lifecycle.
`lifecycles.algorithms.events_all`	Compute all events for a lifecycle object.
`lifecycles.algorithms.event_weights`	Compute the event weights of a target set in a lifecycle object.
`lifecycles.algorithms.facets`	Compute the unicity, identity, and outflow facets of a target set in a lifecycle object.

The module also provides some classical approaches to measure group evolution:

`lifecycles.algorithms.events_asur`	Compute the events in a lifecycle according to Asur et al. Return a dictionary of events of the form {event_type: [event1, event2, ...]}.
`lifecycles.algorithms.event_graph_greene`	Compute the event graph in a lifecycle according to Greene et al. Return a list of match between groups, i.e., edges of the event graph.

Measures¶

lifecycles provides some measures to characterize the structural and semantic evolution of groups.

`lifecycles.algorithms.facet_unicity`	the unicity facet quantifies the extent to which a target set comes from one (=1) or multiple (->0) flows.
`lifecycles.algorithms.facet_identity`	the identity facet quantifies how much the identity of the target set is shared with the reference groups.
`lifecycles.algorithms.facet_outflow`	the outflow facet is the ratio of the number of elements in the target set that are not in any of the reference sets
`lifecycles.algorithms.facet_metadata`	compute the change in attribute entropy between a target set and a reference set
`lifecycles.algorithms.purity`	compute the purity of a set of labels.
`lifecycles.algorithms.event_typicality`	compute the event's name and its typicality score.
`lifecycles.algorithms.stability`	compute the temporal partition stability.