Abstract: Implied relationships between entities, such as network endpoints, are automatically discovered based on co-temporal events. Events involving pairs of endpoints, such as messaging events in which one endpoint acts as a source and another endpoint acts as a destination, may be detected. Edges between nodes representing those endpoints and other nodes representing other endpoints involved in other recent (co-temporal) events may be added to a progressively constructed graph. Over time, such edges may be progressively weighted in response to the detection of further co-temporal events involving the same endpoints. Relationships between endpoints may be implied based on the resulting accumulated weights of edges linking those endpoints' nodes in the graph even if there is no express relationship between those endpoints in any real-word context (e.g., even if those endpoints are not directly connected in any network, and even if no single event involves both of those endpoints together).

Abstract: An extent of relatedness between entities that might exhibit no express relationship is determined based on network communications with shared endpoints. Communications occurring in a network are monitored. For each particular endpoint in the network, a set of other endpoints with which that particular endpoint has communicated is determined based on the monitored communications. For each pair of endpoints in the network, the intersection of the sets determined for those endpoints is determined to be the set of shared endpoints for that pair. The endpoints in the set of shared endpoints can be inversely weighted based on their overall popularity among all of the network's endpoints. The weights of the shared endpoints in the pair's set of shared endpoints can then be multiplied together to produce a relatedness score for that pair of endpoints.

Abstract: Implied relationships between entities, such as network endpoints, are automatically discovered based on co-temporal events. Events involving pairs of endpoints, such as messaging events in which one endpoint acts as a source and another endpoint acts as a destination, may be detected. Edges between nodes representing those endpoints and other nodes representing other endpoints involved in other recent (co-temporal) events may be added to a progressively constructed graph. Over time, such edges may be progressively weighted in response to the detection of further co-temporal events involving the same endpoints. Relationships between endpoints may be implied based on the resulting accumulated weights of edges linking those endpoints' nodes in the graph even if there is no express relationship between those endpoints in any real-word context (e.g., even if those endpoints are not directly connected in any network, and even if no single event involves both of those endpoints together).