Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An arrangement of particles corresponding to the first entities and the second entities may be generated. Charges associated with the particles may be determined based upon the first network profile. The particles may be rearranged to a second arrangement of particles based upon the charges. One or more clusters of particles in the second arrangement of particles may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the one or more clusters of particles.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An arrangement of particles corresponding to the first entities and the second entities may be generated. Charges associated with the particles may be determined based upon the first network profile. The particles may be rearranged to a second arrangement of particles based upon the charges. One or more clusters of particles in the second arrangement of particles may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the one or more clusters of particles.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An arrangement of particles corresponding to the first entities and the second entities may be generated. Charges associated with the particles may be determined based upon the first network profile. The particles may be rearranged to a second arrangement of particles based upon the charges. One or more clusters of particles in the second arrangement of particles may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the one or more clusters of particles.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An arrangement of particles corresponding to the first entities and the second entities may be generated. Charges associated with the particles may be determined based upon the first network profile. The particles may be rearranged to a second arrangement of particles based upon the charges. One or more clusters of particles in the second arrangement of particles may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the one or more clusters of particles.

Abstract: One or more computing devices, systems, and/or methods are provided. First event information associated with a plurality of events may be determined, wherein the plurality of events is associated with a first entity. A set of event metrics associated with the first entity may be determined based upon the first event information. A first combined metric may be determined based upon at least two metrics of the set of event metrics. Whether the first entity is fraudulent may be determined based upon the first combined metric and a threshold metric associated with anomalous behavior.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An arrangement of particles corresponding to the first entities and the second entities may be generated. Charges associated with the particles may be determined based upon the first network profile. The particles may be rearranged to a second arrangement of particles based upon the charges. One or more clusters of particles in the second arrangement of particles may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the one or more clusters of particles.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. First representations associated with the first entities and second representations associated with the second entities may be generated based upon the first network profile. Clusters in the first representations and/or the second representations may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the clusters.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. First representations associated with the first entities and second representations associated with the second entities may be generated based upon the first network profile. Clusters in the first representations and/or the second representations may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the clusters.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.