Abstract: Techniques and solutions are provided for determining changes to networks formed from network objects. Changes to network objects, such as addition or deletion operations are received and associated with timestamps. Last data changes for respective network objects are determined, including a last data change for a particular type associated with a respective networked object, where a networked object may be concurrently associated with a plurality of types. A first network object is removed from a first network, and contributions of the first network object to edges in the first network are removed. A second network object is associated with an addition operation and at least one edge is determined that connects the second network object and a third network object. Sets of disconnected network objects are determined. Network objects are associated with an identifier identifying a network that includes a respective network object.

Abstract: Techniques and solutions are provided for determining changes to networks formed from network objects. Changes to network objects, such as addition or deletion operations are received and associated with timestamps. Last data changes for respective network objects are determined, including a last data change for a particular type associated with a respective networked object, where a networked object may be concurrently associated with a plurality of types. A first network object is removed from a first network, and contributions of the first network object to edges in the first network are removed. A second network object is associated with an addition operation and at least one edge is determined that connects the second network object and a third network object. Sets of disconnected network objects are determined. Network objects are associated with an identifier identifying a network that includes a respective network object.

Abstract: In some embodiments, a method receives a set of objects for creating clusters based on a similarity measure between the objects and selects an object in the set of objects. A plurality of structures is constructed around the object where a size of each structure is a multiple of the similarity measure. The method selects a cluster of objects by analyzing objects within a first structure and a second structure in the plurality of structures around the object. Objects outside of the first structure and the second structure are not analyzed. The cluster of objects are removed from the set of objects. Then, the method performs the selecting of a cluster of objects for another object in the remaining objects in the set of objects and the removing the cluster of objects for the another object until all objects have been added to a cluster.

Abstract: In some embodiments, a method receives a set of objects for creating clusters based on a similarity measure between the objects and generates a data structure for the objects. The method generates link counts for objects by constructing links between objects in the set of objects based on entries between the objects. A link is constructed between the objects when the entry for the respective objects includes a first value. An object is selected that has a largest link count. The link count being a number of objects that are linked to the selected object. A cluster is created including the selected object and any objects that are linked to the selected object via the links. Then, the objects in the cluster are removed from the set of objects and a link count is reduced for objects that are linked to the removed objects. This process is continued for remaining objects.

Abstract: In some embodiments, a method receives a set of objects for creating clusters based on a similarity measure between the objects and selects an object in the set of objects. A plurality of structures is constructed around the object where a size of each structure is a multiple of the similarity measure. The method selects a cluster of objects by analyzing objects within a first structure and a second structure in the plurality of structures around the object. Objects outside of the first structure and the second structure are not analyzed. The cluster of objects are removed from the set of objects. Then, the method performs the selecting of a cluster of objects for another object in the remaining objects in the set of objects and the removing the cluster of objects for the another object until all objects have been added to a cluster.

Abstract: In some embodiments, a method receives a set of objects for creating clusters based on a similarity measure between the objects and generates a data structure for the objects. The method generates link counts for objects by constructing links between objects in the set of objects based on entries between the objects. A link is constructed between the objects when the entry for the respective objects includes a first value. An object is selected that has a largest link count. The link count being a number of objects that are linked to the selected object. A cluster is created including the selected object and any objects that are linked to the selected object via the links. Then, the objects in the cluster are removed from the set of objects and a link count is reduced for objects that are linked to the removed objects. This process is continued for remaining objects.

Abstract: A set of objects is defined from a plurality of objects. The objects are defined with a common structure including properties. The plurality of objects is to be clustered into clusters. A clustering criterion for determining the clusters is defined. The clusters are non-intersecting sets of objects from the set of objects. Object distance between a first object and a second object from the set of objects is computed. The computation of the object distance is based on computation of distances between property values defined for properties from the structure of the objects from the set. When the first object is a part of the cluster, the second objects is added to the cluster when the object distance complies with the clustering criterion. The clusters are determined in a number of iterations based on evaluations of the distances between objects from subsequently determined subsets of objects from the plurality.