With the proliferation of online social sites (web sites with social features), a typical user may maintain several user accounts across multiple social sites.
Currently, these social sites operate as isolated islands. In fact, most of these sites are very protective about the information on their site.
However, knowledge of a user’s set of social identities (different identities used by the user on different social sites) is useful for improving the performance of applications that process and present information to users. Typically, an online social service determines additional identities of a user from information the user has provided explicitly, but a user may not provide a full list of all the user’s alternate identities to any particular social service.
Google has patented a method for social identity clustering. The method includes receiving a connection graph representing public social data, where the nodes represent social identities and each edge is either a “me” edge between identities claimed to belong to the same person or a “friend” edge between identities claimed to belong to different identities. The method further includes converting the graph to a cluster graph in which each node initially corresponds to a single node of the connection graph. The method further includes updating the cluster graph by iteratively merging cluster nodes based on an analysis of the weight of the “me” edges connecting them, and then replacing the merged cluster nodes within the graph with a new cluster node containing the merged cluster nodes, where the edges of the new cluster node are the aggregated edges of the merged cluster nodes.
Patent Number: 9,064,002
Patent Tilte: Social identity clustering
Inventors: Gyongyi; Zoltan I. (Menlo Park, CA), Heymans; Maureen (San Francisco, CA), Lopyrev; Mikhail (San Francisco, CA), Roehrig; Hein (San Francisco, CA)
Assignee: Google Inc. (Mountain View, CA)
Family ID: 1000000421607
Appl. No.: 14/093,984
Filed: December 2, 2013
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for social identity clustering. In one aspect, a method includes receiving a connection graph representing public social data, where the nodes represent social identities and each edge is either a “me” edge between identities claimed to belong to the same person or a “friend” edge between identities claimed to belong to different identities. The method further includes converting the graph to a cluster graph in which each node initially corresponds to a single node of the connection graph. The method further includes updating the cluster graph by iteratively merging cluster nodes based on an analysis of the weight of the “me” edges connecting them, and then replacing the merged cluster nodes within the graph with a new cluster node containing the merged cluster nodes, where the edges of the new cluster node are the aggregated edges of the merged cluster nodes.