公开(公告)号：US20210342345A1

公开(公告)日：2021-11-04

申请号：US17373281

申请日：2021-07-12

Applicant: ADOBE INC.

Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC: G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US11113293B2

公开(公告)日：2021-09-07

申请号：US16252169

申请日：2019-01-18

Applicant: ADOBE INC.

Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC: G06F16/24 ,  G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US11860675B2

公开(公告)日：2024-01-02

申请号：US17373281

申请日：2021-07-12

Applicant: ADOBE INC.

Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC: G06F16/24 ,  G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

CPC classification number: G06F16/2465 ,  G06F16/215 ,  G06F16/26 ,  G06F16/288 ,  G06F16/9024 ,  G06F2216/03

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US11621892B2

公开(公告)日：2023-04-04

申请号：US17095070

申请日：2020-11-11

Applicant: Adobe Inc.

Inventor： Sungchul Kim ,  Di Jin ,  Ryan A. Rossi ,  Eunyee Koh

IPC: H04L41/12 ,  H04L43/067 ,  G06F16/901 ,  H04L41/14 ,  H04L43/045

Abstract: Deriving network embeddings that represent attributes of, and relationships between, different nodes in a network while preserving network data temporal and structural properties is described. A network representation system generates a plurality of graph time-series representations of network data that each includes a subset of nodes and edges included in a time segment of the network data, constrained either by time or a number of edges included in the representation. A temporal graph of the network data is generated by implementing a temporal model that incorporates temporal dependencies into the graph time-series representations. From the temporal graph, network embeddings for the network data are derived, where the network embeddings capture temporal dependencies between nodes, as indicated by connecting edges, as well as temporal structural properties of the network data. Network embeddings represent network data in a low-dimensional latent space, which is useable to generate a prediction regarding the network data.

公开(公告)号：US20220150123A1

公开(公告)日：2022-05-12

申请号：US17095070

申请日：2020-11-11

Applicant: Adobe Inc.

Inventor： Sungchul Kim ,  Di Jin ,  Ryan A. Rossi ,  Eunyee Koh

IPC: H04L12/24 ,  H04L12/26 ,  G06F16/901

Abstract: Deriving network embeddings that represent attributes of, and relationships between, different nodes in a network while preserving network data temporal and structural properties is described. A network representation system generates a plurality of graph time-series representations of network data that each includes a subset of nodes and edges included in a time segment of the network data, constrained either by time or a number of edges included in the representation. A temporal graph of the network data is generated by implementing a temporal model that incorporates temporal dependencies into the graph time-series representations. From the temporal graph, network embeddings for the network data are derived, where the network embeddings capture temporal dependencies between nodes, as indicated by connecting edges, as well as temporal structural properties of the network data. Network embeddings represent network data in a low-dimensional latent space, which is useable to generate a prediction regarding the network data.

公开(公告)号：US20200233864A1

公开(公告)日：2020-07-23

申请号：US16252169

申请日：2019-01-18

Applicant: ADOBE INC.

Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC: G06F16/2458 ,  G06F16/901 ,  G06F16/28 ,  G06F16/26 ,  G06F16/215

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.