公开(公告)号：US20200342006A1

公开(公告)日：2020-10-29

申请号：US16397839

申请日：2019-04-29

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Eunyee Koh ,  Anup Bandigadi Rao ,  Aldo Gael Carranza

IPC: G06F16/28 ,  G06F16/22

Abstract: In implementations of higher-order graph clustering and embedding, a computing device receives a heterogeneous graph representing a network. The heterogeneous graph includes nodes that each represent a network entity and edges that each represent an association between two of the nodes in the heterogeneous graph. To preserve node-type and edge-type information, a typed graphlet is implemented to capture a connectivity pattern and the types of the nodes and edges. The computing device determines a frequency of the typed graphlet in the graph and derives a weighted typed graphlet matrix to sort graph nodes. Sorted nodes are subsequently analyzed to identify node clusters having a minimum typed graphlet conductance score. The computing device is further implemented to determine a higher-order network embedding for each of the nodes in the graph using the typed graphlet matrix, which can then be concatenated into a matrix representation of the network.

公开(公告)号：US11170048B2

公开(公告)日：2021-11-09

申请号：US16451956

申请日：2019-06-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Aldo Gael Carranza ,  David Arbour ,  Anup Rao ,  Sungchul Kim ,  Eunyee Koh

IPC: G06F16/00 ,  G06F16/901 ,  G06F17/18

Abstract: A system is disclosed for identifying and counting typed graphlets in a heterogeneous network. A methodology implementing techniques for the disclosed system according to an embodiment includes identifying typed k-node graphlets occurring between any two selected nodes of a heterogeneous network, wherein the nodes are connected by one or more edges. The identification is based on combinatorial relationships between (k−1)-node typed graphlets occurring between the two selected nodes of the heterogeneous network. Identification of 3-node typed graphlets is based on computation of typed triangles, typed 3-node stars, and typed 3-paths associated with each edge connecting the selected nodes. The method further includes maintaining a count of the identified k-node typed graphlets and storing those graphlets with non-zero counts. The identified graphlets are employed for applications including visitor stitching, user profiling, outlier detection, and link prediction.

公开(公告)号：US11163803B2

公开(公告)日：2021-11-02

申请号：US16397839

申请日：2019-04-29

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Eunyee Koh ,  Anup Bandigadi Rao ,  Aldo Gael Carranza

IPC: G06F16/28 ,  G06F16/22

Abstract: In implementations of higher-order graph clustering and embedding, a computing device receives a heterogeneous graph representing a network. The heterogeneous graph includes nodes that each represent a network entity and edges that each represent an association between two of the nodes in the heterogeneous graph. To preserve node-type and edge-type information, a typed graphlet is implemented to capture a connectivity pattern and the types of the nodes and edges. The computing device determines a frequency of the typed graphlet in the graph and derives a weighted typed graphlet matrix to sort graph nodes. Sorted nodes are subsequently analyzed to identify node clusters having a minimum typed graphlet conductance score. The computing device is further implemented to determine a higher-order network embedding for each of the nodes in the graph using the typed graphlet matrix, which can then be concatenated into a matrix representation of the network.