Abstract: Disclosed examples include at least one memory, instructions, and processor circuitry to execute the instructions to generate a device graph, the device graph to represent links between ones of personally identifiable information nodes and ones of device nodes, generate person-clusters based on the device graph, the person-clusters based on the links and community detection hyperparameter values, generate a node-to-person lookup structure based on the person-clusters, and deduplicate impression data based on the node-to-person lookup structure.

Abstract: Methods, apparatus, systems, and articles of manufacture for user identification via community detection are disclosed. Example instructions, when executed, cause at least one processor to at least access personally identifiable information to device links, build a device graph based on the personally identifiable information to device links, split components of the device graph into person clusters using community detection, create a snapshot including a device-to-person link lookup, and prepare a person-level impression measurement report from the snapshot.

Abstract: Methods, apparatus, systems, and articles of manufacture for user identification via community detection are disclosed. Example instructions, when executed, cause at least one processor to at least access personally identifiable information to device links, build a device graph based on the personally identifiable information to device links, split components of the device graph into person clusters using community detection, create a snapshot including a device-to-person link lookup, and prepare a person-level impression measurement report from the snapshot.

Abstract: Methods, apparatus, systems, and articles of manufacture for user identification via community detection are disclosed. Example instructions, when executed, cause at least one processor to at least access personally identifiable information to device links, build a device graph based on the personally identifiable information to device links, split components of the device graph into person clusters using community detection, create a snapshot including a device-to-person link lookup, and prepare a person-level impression measurement report from the snapshot.

Abstract: Methods, apparatus, systems, and articles of manufacture for user identification via community detection are disclosed. Example instructions, when executed, cause at least one processor to at least access personally identifiable information to device links, build a device graph based on the personally identifiable information to device links, split components of the device graph into person clusters using community detection, create a snapshot including a device-to-person link lookup, and prepare a person-level impression measurement report from the snapshot.

Abstract: Disclosed examples include at least one memory, instructions, and processor circuitry to execute the instructions to generate a device graph, the device graph to represent links between ones of personally identifiable information nodes and ones of device nodes, generate person-clusters based on the device graph, the person-clusters based on the links and community detection hyperparameter values, generate a node-to-person lookup structure based on the person-clusters, and deduplicate impression data based on the node-to-person lookup structure.

Abstract: Methods, apparatus, systems, and articles of manufacture for user identification via community detection are disclosed. Example instructions, when executed, cause at least one processor to at least access personally identifiable information to device links, build a device graph based on the personally identifiable information to device links, split components of the device graph into person clusters using community detection, create a snapshot including a device-to-person link lookup, and prepare a person-level impression measurement report from the snapshot.