Tracking cluster identity over time

    公开(公告)号:US11539595B1

    公开(公告)日:2022-12-27

    申请号:US16805520

    申请日:2020-02-28

    Abstract: A real-time stream-based clustering algorithm is disclosed for correlating network impact according to time and space. The clustering algorithm operates at discrete time steps and produces a partitioning of a network graph such that each partition is a cluster. Clusters are tracked at each time step and the partitions can change by disappearing, splitting or merging with others. To track an incident over many clustering time steps, an ID is assigned to and related to previous clusters such that the same ID can propagate between multiple cluster time steps. Thus, a same incident can be tracked over time as its effect traverses the network. Anchor nodes can be assigned to the clusters to establish a relationship between clusters at different time steps.

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