Analyzing behavior in light of social time
A relational event history is determined based on a data set, the relational event history including a set of relational events that occurred in time among a set of actors. Data is populated in a probability model based on the relational event history, where the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, where the probability model includes one or more statistical parameters and corresponding statistics. A baseline communications behavior for the relational event history is determined based on the populated probability model, and departures within the relational event history from the baseline communications behavior are determined.
1. A method for analyzing behavior in light of social time comprising:
receiving data from a plurality of data sources, the plurality of data sources comprising an event data source, the event data source providing information regarding a plurality of events;
deriving an event history form the plurality of events, the event history comprising a social time representation of the plurality of events, the social time representation of the plurality of events considering time within the event history to progress only when events occur;
normalizing the data from the plurality of data sources to provide normalized data;
determining a model to apply to the normalized data, the model comprising a probability model, the probability model being formulated as a series of conditional probabilities corresponding to a set of decisions by an actor for each of the plurality of events, the probability model including a statistical parameter and corresponding statistics;
determining, based on the probability model, a baseline for the event history, the baseline comprising a first set of values for the statistical parameter;
drawing inferences from the model; and,
using the inferences to determine whether behavior associated with the plurality of events represents a departure from a baseline behavior based upon a given event history, determining the departure from the baseline behavior comprising
determining, based on one or more subsets of the events included in the event history, a second set of values for the statistical parameter; and
comparing the second set of values for the statistical parameter to the first set of values, comparing the second set of values for the statistical parameters comprises determining a hypothesis regarding communications behavior within the event history and testing the hypothesis using the second set of values.
2. The method of claim 1 , wherein:
the plurality of events comprise communications events.
3. The method of claim 1 , wherein:
the plurality of data sources comprise a data source having information relating to an organization and personnel of the organization.
4. The method of claim 1 , wherein:
the normalized data comprises covariate data.
5. The method of claim 4 , further comprising:
populating the probability model based upon the event history and the covariate data;
fitting a probability model to the event history.
6. The method of claim 4 , further comprising:
using the probability model to identify patterns, changes and anomalies within the event history.
7. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving data from a plurality of data sources, the plurality of data sources comprising an event data source, the event data source providing information regarding a plurality of events;
deriving an event history form the plurality of events, the event history comprising a social time representation of the plurality of events, the social time representation of the plurality of events considering time within the event history to progress only when events occur;
normalizing the data from the plurality of data sources to provide normalized data;
determining a model to apply to the normalized data, the model comprising a probability model, the probability model being formulated as a series of conditional probabilities corresponding to a set of decisions by an actor for each of the plurality of events, the probability model including a statistical parameter and corresponding statistics;
drawing inferences from the model; and,
using the inferences to determine whether behavior associated with the plurality of events represents a departure from a baseline behavior based upon a given event history, determining the departure from the baseline behavior comprising
determining, based on one or more subsets of the events included in the event history, a second set of values for the statistical parameter; and
comparing the second set of values for the statistical parameter to the first set of values, comparing the second set of values for the statistical parameters comprises determining a hypothesis regarding communications behavior within the event history and testing the hypothesis using the second set of values.
8. The system of claim 7 , wherein:
the plurality of events comprise communications events.
9. The system of claim 7 , wherein:
the plurality of data sources comprise a data source having information relating to an organization and personnel of the organization.
10. The system of claim 7 , wherein:
the normalized data comprises covariate data.
11. The system of claim 10 , wherein the instructions are further configured for:
populating the probability model based upon the event history and the covariate data;
fitting a probability model to the event history.
12. The system of claim 7 , wherein the instructions are further configured for:
using the probability model to identify patterns, changes and anomalies within the event history.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving data from a plurality of data sources, the plurality of data sources comprising an event data source, the event data source providing information regarding a plurality of events;
deriving an event history form the plurality of events, the event history comprising a social time representation of the plurality of events, the social time representation of the plurality of events considering time within the event history to progress only when events occur;
normalizing the data from the plurality of data sources to provide normalized data;
determining a model to apply to the normalized data, the model comprising a probability model, the probability model being formulated as a series of conditional probabilities corresponding to a set of decisions by an actor for each of the plurality of events, the probability model including a statistical parameter and corresponding statistics;
drawing inferences from the model; and,
using the inferences to determine whether behavior associated with the plurality of events represents a departure from a baseline behavior based upon a given event history, determining the departure from the baseline behavior comprising
determining, based on one or more subsets of the events included in the event history, a second set of values for the statistical parameter; and
comparing the second set of values for the statistical parameter to the first set of values, comparing the second set of values for the statistical parameters comprises determining a hypothesis regarding communications behavior within the event history and testing the hypothesis using the second set of values.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the plurality of events comprise communications events.
15. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the plurality of data sources comprise a data source having information relating to an organization and personnel of the organization.
16. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the normalized data comprises covariate data.
17. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:
populating the probability model based upon the event history and the covariate data;
fitting a probability model to the event history.
18. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:
using the probability model to identify patterns, changes and anomalies within the event history.