公开(公告)号：US12136025B1

公开(公告)日：2024-11-05

申请号：US17884149

申请日：2022-08-09

Applicant: Google LLC

Inventor： Rami Al-Rfou′ ,  Sami Ahmad Abu-El-Haija ,  Bryan Thomas Perozzi

IPC: G06N3/04 ,  G06F16/901 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a graph processing system. In one aspect, the graph processing system obtains data identifying a first node and a second node from a graph of nodes and edges. The system processes numeric embeddings of the first node and the second node using a manifold neural network to generate respective manifold coordinates of the first node and the second node. The system applies a learned edge function to the manifold coordinates of the first node and the manifold coordinates of the second node to generate an edge score that represents a likelihood that an entity represented by the first node and an entity represented by the second node have a particular relationship.

公开(公告)号：US20240386241A1

公开(公告)日：2024-11-21

申请号：US18664164

申请日：2024-05-14

Applicant: Google LLC

Inventor： Brandon Asher Mayer ,  Bryan Thomas Perozzi ,  Hendrik Fichtenberger ,  Anton Tsitsulin ,  Jonathan Jesse Halcrow

IPC: G06N3/042 ,  G06N3/08

Abstract: A distributed computing system is configured to perform operations for embedding graphs of large scale. The system can generate node sequences from a target graph, determine training samples, and perform unsupervised learning using counts of co-occurrences between nodes to iteratively update an embedding table and learn a low-dimensional representation of the graph.

公开(公告)号：US20230267302A1

公开(公告)日：2023-08-24

申请号：US17940568

申请日：2022-09-08

Applicant: Google LLC

Inventor： Bryan Thomas Perozzi ,  Anton Tsitsulin ,  John Joseph Palowitch ,  Brandon Mayer

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: Systems and methods for graph model search and/or for architecture insight can include training and testing a plurality of graph models. For example, the systems and methods can generate a plurality of synthetic graph datasets, which can then be utilized to train a plurality of graph models with varying graph model architectures. The trained graph models can then be evaluated based on outputs generated by the models based on test inputs. The evaluation data can then be utilized for providing particular graph model insight and/or may be utilized to enable task-specific graph model search.

公开(公告)号：US11455512B1

公开(公告)日：2022-09-27

申请号：US15946301

申请日：2018-04-05

Applicant: Google LLC

Inventor： Rami Al-rfou′ ,  Sami Ahmad Abu-El-Haija ,  Bryan Thomas Perozzi

IPC: G06N3/04 ,  G06N3/08 ,  G06F16/901

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a graph processing system. In one aspect, the graph processing system obtains data identifying a first node and a second node from a graph of nodes and edges. The system processes numeric embeddings of the first node and the second node using a manifold neural network to generate respective manifold coordinates of the first node and the second node. The system applies a learned edge function to the manifold coordinates of the first node and the manifold coordinates of the second node to generate an edge score that represents a likelihood that an entity represented by the first node and an entity represented by the second node have a particular relationship.