MODELING SOCIAL BEHAVIOR

1 . A method comprising:

determining a relational event history based on a data set, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating data in a probability model based on the relational event history, 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, the probability model including one or more statistical parameters and corresponding statistics that relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events;

determining, by one or more processing devices, a baseline communications behavior for the relational event history based on the populated probability model, 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 determining departures from the baseline communications behavior within the relational event history comprises determining a second set of values for the statistical parameters based on one or more subsets of the relational events included in the relational event history and comparing the second set of values for the statistical parameters to the first set of values.

3 . The method of claim 2 , 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.

4 . The method of claim 1 , wherein the set of sequential decisions comprises a decision to send a communication, a decision as to a mode of the communication, a decision as to a topic of the communication, and one or more decisions as to recipients of the communication.

5 . The method of claim 1 , wherein determining the relational event history based on the data set comprises identifying, for each relational event in a set of relational events included in the data set, a sender, a topic, a mode, and one or more recipients.

6 . The method of claim 1 , wherein generating the relational event history comprises:

extracting data from the data set;

transforming the extracted data;

loading the transformed data; and

enriching the loaded data.

7 . The method of claim 1 , further comprising determining one or more covariates based on the data set, wherein the probability model includes the one or more covariates.

8 . The method of claim 7 , wherein the one or more covariates include one or more global covariates, actor covariates, or dyadic covariates, wherein a global covariate has a single value for all actors or pairs of actors that are included in the set of actors, wherein an actor covariate has a different value for each actor that is included in the set of actors, and wherein a dyadic covariate has a different value for each pair of actors that is included in the set of actors.

9 . The method of claim 1 , wherein the baseline communications behavior for the relational event history is determined using a maximum-likelihood method.

10 . The method of claim 1 , wherein the baseline communications behavior for the relational event history is determined using a distributed computing platform.

11 . 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 a relational event history based on a data set, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating data in a probability model based on the relational event history, 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, the probability model including one or more statistical parameters and corresponding statistics that relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events;

determining, by one or more processing devices, a baseline communications behavior for the relational event history based on the populated probability model, 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.

12 . The system of claim 11 , wherein determining departures from the baseline communications behavior within the relational event history comprises determining a second set of values for the statistical parameters based on one or more subsets of the relational events included in the relational event history 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 11 , wherein the set of sequential decisions comprises a decision to send a communication, a decision as to a mode of the communication, a decision as to a topic of the communication, and one or more decisions as to recipients of the communication.

15 . The system of claim 11 , wherein determining the relational event history based on the data set comprises identifying, for each relational event in a set of relational events included in the data set, a sender, a topic, a mode, and one or more recipients.

16 . The system of claim 11 , wherein generating 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 11 , further comprising determining one or more covariates based on the data set, wherein the probability model includes the one or more covariates.

18 . The system of claim 16 , wherein the one or more covariates include one or more global covariates, actor covariates, or dyadic covariates, wherein a global covariate has a single value for all actors or pairs of actors that are included in the set of actors, wherein an actor covariate has a different value for each actor that is included in the set of actors, and wherein a dyadic covariate has a different value for each pair of actors that is included in the set of actors.

19 . The system of claim 11 , wherein the baseline communications behavior for the relational event history is determined using a maximum-likelihood method.

20 . The system of claim 11 , wherein the baseline communications behavior for the relational event history is determined using a distributed computing platform.

21 . 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 a relational event history based on a data set, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating data in a probability model based on the relational event history, 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, the probability model including one or more statistical parameters and corresponding statistics that relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events;

determining, by one or more processing devices, a baseline communications behavior for the relational event history based on the populated probability model, 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.