Method and system for selecting a target with respect to a behavior in a population of communicating entities

A data analysis system for selecting a target with respect to a specific behavior as a group of entities in a population of communicating entities, wherein a social network representation is used for the population of communicating entities in a plurality of observation periods, such that, for an observation period, a social network has nodes respectively representing the entities of the population and links between the nodes, each link between two nodes representing at least one communication event observed in said observation period between the entities represented by said two nodes, each node being associated with a respective set of at least one node connected thereto by one of the links, the system comprising:

• a behavioral centrality evaluator for receiving a first social network for a first observation period and behavioral data indicating adoption of the specific behavior by entities of the population in a time period following the first observation period, and computing respective behavioral centrality measures for the nodes of the first social network, wherein a behavioral centrality measure for one of the nodes depends on adoption or non-adoption of said behavior in said time period by each entity of the population represented by a connected node of the set associated with said one of the nodes;
• a modeling unit for building a predictive model having input data and first predicted behavioral centrality measures as output data, the predictive model being determined to provide a best match of the computed behavioral centrality measures with first predicted behavioral centrality measures resulting from application of the predictive model to input data from the first social network;
• a behavioral centrality predictor for receiving a second social network for a second observation period more recent than the first observation period, and applying the predictive model to input data from the second social network to provide second predicted behavioral centrality measures;
• and
• a selector for selecting entities to be in the target based on information including the second predicted behavioral centrality measures,
• wherein the behavioral centrality measures include, for each node i of the first social network, a respective measure computed as a sum of terms aij×Bj for nodes j≠i belonging to the set of connected nodes associated with node i, where aij is a weight associated with the link between nodes i and j, Bj=1 if node j is associated with an entity that adopted said behavior in said time period according to the behavioral data and Bj=0 else.

A non-transitory computer-readable medium having computer program instructions stored thereon for carrying out steps of a method of selecting a target with respect to a specific behavior when said instructions are executed in a computer processing unit of a data analysis system, the target being selected as a group of entities in a population of communicating entities,

• wherein a social network representation is used for the population of communicating entities in a plurality of observation periods, such that, for an observation period, a social network has nodes respectively representing the entities of the population and links between the nodes, each link between two nodes representing at least one communication event observed in said observation period between the entities represented by said two nodes, each node being associated with a respective set of at least one node connected thereto by one of the links, said steps comprising:
• obtaining a first social network for a first observation period;
• obtaining behavioral data indicating adoption of the specific behavior by entities of the population in a time period following the first observation period;
• computing respective behavioral centrality measures for the nodes of the first social network, wherein a behavioral centrality measure for one of the nodes depends on adoption or non-adoption of said behavior in said time period by each entity of the population represented by a connected node of the set associated with said one of the nodes;
• building a predictive model having input data and first predicted behavioral centrality measures as output data, the predictive model being determined to provide a best match of the computed behavioral centrality measures with first predicted behavioral centrality measures resulting from application of the predictive model to input data from the first social network;
• obtaining a second social network for a second observation period more recent than the first observation period;
• applying the predictive model to input data from the second social network to provide second predicted behavioral centrality measures;
• and
• selecting entities to be in the target based on information including the second predicted behavioral centrality measures,
• wherein the behavioral centrality measures include, for each node i of the first social network, a respective measure computed as a sum of terms aij×Bj for nodes j≠i belonging to the set of connected nodes associated with node i, where aij is a weight associated with the link between nodes i and j, Bj=1 if node j is associated with an entity that adopted said behavior in said time period according to the behavioral data and Bj=0 else.

A method of selecting a target with respect to a specific behavior as a group of entities in a population of communicating entities,

• wherein a social network representation is used for the population of communicating entities in a plurality of observation periods, such that, for an observation period, a social network has nodes respectively representing the entities of the population and links between the nodes, each link between two nodes representing at least one communication event observed in said observation period between the entities represented by said two nodes, each node being associated with a respective set of at least one node connected thereto by one of the links,
• wherein the links are directed in the social network representation such that, for an observation period, each link from a first node to a second node represents at least one communication event observed in said observation period from the entity represented by the first node to the entity represented by the second node, and wherein, for one node of the first social network, the associated set of connected nodes consists of any other nodes of the first social network such that the first social network has a link from said one node to said other node,
• the method comprising:
• obtaining a first social network for a first observation period;
• obtaining behavioral data indicating adoption of the specific behavior by entities of the population in a time period following the first observation period;
• computing respective behavioral centrality measures for the nodes of the first social network, wherein a behavioral centrality measure for one of the nodes depends on adoption or non-adoption of said behavior in said time period by each entity of the population represented by a connected node of the set associated with said one of the nodes;
• determining influence cascades originating from respective nodes of the first social network;
• building a predictive model having input data and first predicted behavioral centrality measures as output data, the predictive model being determined to provide a best match of the computed behavioral centrality measures with first predicted behavioral centrality measures resulting from application of the predictive model to input data from the first social network;
• obtaining a second social network for a second observation period more recent than the first observation period;
• applying the predictive model to input data from the second social network to provide second predicted behavioral centrality measures;
• and
• selecting entities to be in the target based on information including the second predicted behavioral centrality measures,
• wherein the influence cascade originating from a node j0 is a sequence of distinct nodes j1, j2, . . . , jk of the first social network for a positive integer k, such that:
• for any p=0, . . . , k−1, the first social network has a link from node jp to node jp+1;
• the entity represented by node ji in the first social network adopted said behavior in said time period according to the behavioral data;
• and
• for any p=1, . . . , k−1, the entity represented by node jp+1 in the first social network adopted said behavior after the entity represented by node jp in said time period according to the behavioral data.

A non-transitory computer-readable medium having computer program instructions stored thereon for carrying out steps of a method of selecting a target with respect to a specific behavior when said instructions are executed in a computer processing unit of a data analysis system, the target being selected as a group of entities in a population of communicating entities,

• wherein a social network representation is used for the population of communicating entities in a plurality of observation periods, such that, for an observation period, a social network has nodes respectively representing the entities of the population and links between the nodes, each link between two nodes representing at least one communication event observed in said observation period between the entities represented by said two nodes, each node being associated with a respective set of at least one node connected thereto by one of the links,
• wherein the links are directed in the social network representation such that, for an observation period, each link from a first node to a second node represents at least one communication event observed in said observation period from the entity represented by the first node to the entity represented by the second node, and wherein, for one node of the first social network, the associated set of connected nodes consists of any other nodes of the first social network such that the first social network has a link from said one node to said other node,
• said steps comprising:
• obtaining a first social network for a first observation period;
• obtaining behavioral data indicating adoption of the specific behavior by entities of the population in a time period following the first observation period;
• determining influence cascades originating from respective nodes of the first social network
• computing respective behavioral centrality measures for the nodes of the first social network, wherein a behavioral centrality measure for one of the nodes depends on adoption or non-adoption of said behavior in said time period by each entity of the population represented by a connected node of the set associated with said one of the nodes;
• building a predictive model having input data and first predicted behavioral centrality measures as output data, the predictive model being determined to provide a best match of the computed behavioral centrality measures with first predicted behavioral centrality measures resulting from application of the predictive model to input data from the first social network;
• obtaining a second social network for a second observation period more recent than the first observation period;
• applying the predictive model to input data from the second social network to provide second predicted behavioral centrality measures;
• and
• selecting entities to be in the target based on information including the second predicted behavioral centrality measures,
• wherein the influence cascade originating from a node j0 is a sequence of distinct nodes j1, j2, . . . , jk of the first social network for a positive integer k, such that:
• for any p=0, . . . , k−1, the first social network has a link from node jp to node jp+1;
• the entity represented by node ji in the first social network adopted said behavior in said time period according to the behavioral data;
• and
• for any p=1, . . . , k−1, the entity represented by node jp+1 in the first social network adopted said behavior after the entity represented by node jp in said time period according to the behavioral data.