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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors; populating, based on the relational event history, data in a probability model, wherein 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, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics; determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and determining departures from the baseline communications behavior within the relational event history. 2. The method of claim 1, wherein the one or more corresponding statistics relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events. 3. The method of claim 1, wherein determining departures from the baseline communications behavior within the relational event history comprises: determining, based on one or more subsets of the relational events included in the relational event history, a second set of values for the statistical parameters; and comparing the second set of values for the statistical parameters to the first set of values. 4. The method of claim 3, wherein comparing the second set of values for the statistical parameters to the first set of values comprises: determining a hypothesis regarding communications behavior within the relational event history; and testing the hypothesis using the second set of values. 5. The method of claim 4, wherein testing the hypothesis using the second set of values comprises: computing, based on the second set of values, a value of a test statistic; and using the test statistic to determine the departures from the baseline communications behavior. 6. The method of claim 4, wherein determining the hypothesis regarding communications behavior within the relational event history comprises selecting a set of predictions regarding differences in communications behavior with respect to one or more of the statistical parameters over a period of time. 7. The method of claim 1, wherein determining the relational event history comprises: extracting data from the data set; transforming the extracted data; loading the transformed data; and enriching the loaded data. 8. The method of claim 1, further comprising determining a subset of actors included in the set of actors, wherein at least one decision included in the set of sequential decisions comprises selecting a recipient of the relational event from the subset of actors. 9. The method of claim 1, further comprising: receiving one or more user inputs; outputting, based on the one or more user inputs, a graphical analysis of the baseline communications behavior; and outputting, based on the one or more user inputs, one or more graphical analyses of the departures from the baseline communications behavior. 10. A system comprising: one or more processing devices; and one or more non-transitory computer-readable media coupled to the one or more processing devices having instructions stored thereon which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising: determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors; populating, based on the relational event history, data in a probability model, wherein 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, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics; determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and determining departures from the baseline communications behavior within the relational event history. 11. The system of claim 10, wherein the one or more corresponding statistics relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events. 12. The system of claim 10, wherein determining departures from the baseline communications behavior within the relational event history comprises: determining, based on one or more subsets of the relational events included in the relational event history, a second set of values for the statistical parameters; and comparing the second set of values for the statistical parameters to the first set of values. 13. The system of claim 12, wherein comparing the second set of values for the statistical parameters to the first set of values comprises: determining a hypothesis regarding communications behavior within the relational event history; and testing the hypothesis using the second set of values. 14. The system of claim 13, wherein testing the hypothesis using the second set of values comprises: computing, based on the second set of values, a value of a test statistic; and using the test statistic to determine the departures from the baseline communications behavior. 15. The system of claim 13, wherein determining the hypothesis regarding communications behavior within the relational event history comprises selecting a set of predictions regarding differences in communications behavior with respect to one or more of the statistical parameters over a period of time. 16. The system of claim 10, wherein determining the relational event history comprises: extracting data from the data set; transforming the extracted data; loading the transformed data; and enriching the loaded data. 17. The system of claim 10, the operations further comprising determining a subset of actors included in the set of actors, wherein at least one decision included in the set of sequential decisions comprises selecting a recipient of the relational event from the subset of actors. 18. The system of claim 10, the operations further comprising: receiving one or more user inputs; outputting, based on the one or more user inputs, a graphical analysis of the baseline communications behavior; and outputting, based on the one or more user inputs, one or more graphical analyses of the departures from the baseline communications behavior. 19. A non-transitory computer-readable medium embodying one or more instructions thereon which, when executed, cause one or more computer processors to perform steps comprising: determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors; populating, based on the relational event history, data in a probability model, wherein 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, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics; determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and determining departures from the baseline communications behavior within the relational event history. 20. The computer-readable medium of claim 19, wherein the one or more corresponding statistics relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events.