公开(公告)号：US11809993B2

公开(公告)日：2023-11-07

申请号：US16850570

申请日：2020-04-16

Applicant: Google LLC

Inventor： Rami Al-Rfou ,  Dustin Zelle ,  Bryan Perozzi

IPC: G06N3/084 ,  G06N20/20 ,  G06N3/02 ,  G06F18/22 ,  G06N3/045 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06F18/22 ,  G06N3/02 ,  G06N3/045 ,  G06N20/20 ,  G06V10/82

Abstract: The present disclosure provides computing systems and methods directed to algorithms and the underlying machine learning (ML) models for evaluating similarity between graphs using graph structures and/or attributes. The systems and methods disclosed may provide advantages or improvements for comparing graphs without additional context or input from a person (e.g., the methods are unsupervised). In particular, the systems and methods of the present disclosure can operate to generate respective embeddings for one or more target graphs, where the embedding for each target graph is indicative of a respective similarity of such target graph to each of a set of source graphs, and where a pair of embeddings for a pair of target graphs can be used to assess a similarity between the pair of target graphs.

公开(公告)号：US20230214425A1

公开(公告)日：2023-07-06

申请号：US17927494

申请日：2020-09-24

Applicant: Google LLC

Inventor： Bryan Perozzi ,  Anton Tsitsulin ,  Silvio Lattanzi ,  Filipe Miguel Conçalves de Almeida ,  Yingtao Tian ,  Stefan Postavaru

IPC: G06F16/901

CPC classification number: G06F16/9024

Abstract: Systems and methods for generating single-node representations in graphs comprised of linked nodes. The present technology enables generation of individual node embeddings on the fly in sublinear time (less than O(n), where n is the number of nodes in graph G) using only a PPR vector for the node, and random projection to reduce the dimensionality of the node’s PPR vector. In one example, the present technology includes a computer-implemented method comprising obtaining a graph having a plurality of nodes from a database, generating a personal pagerank vector for a given node of the plurality of nodes, and producing an embedding vector for the given node by randomly projecting the personal pagerank vector, wherein the embedding vector has lower dimensionality than the personal pagerank vector.

公开(公告)号：US12099925B1

公开(公告)日：2024-09-24

申请号：US17072592

申请日：2020-10-16

Applicant: Google LLC

Inventor： Bryan Perozzi ,  Yingtao Tian

IPC: G06N3/08 ,  G06N3/044 ,  G06N7/01

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N7/01

Abstract: Systems, methods, and computer readable media related to training and/or utilizing a neural network model to determine, based on a sequence of sources that each have an electronic interaction with a given electronic resource, one or more subsequent source(s) for interaction with the given electronic resource. For example, source representations of those sources can be sequentially applied (in an order that conforms to the sequence) as input to a trained recurrent neural network model, and output generated over the trained recurrent neural network model based on the applied input. The generated output can indicate, for each of a plurality of additional sources, a probability that the additional source will subsequently (e.g., next) interact with the given electronic resource. Such probabilities indicated by the output can be utilized in performance of further electronic action(s) related to the given electronic resource.

公开(公告)号：US11100688B2

公开(公告)日：2021-08-24

申请号：US16523612

申请日：2019-07-26

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Bryan Perozzi

IPC: G06F16/901 ,  G06T9/00 ,  G06T11/20

Abstract: The present disclosure is directed to encoding graphs. In particular, the methods and systems of the present disclosure can: receive data describing a first graph; and for each node, of one or more nodes, of the first graph, determine, based at least in part on data describing a second graph, and for each of multiple nodes of the second graph corresponding to the node of the first graph, a representation of a role of the node of the multiple nodes in a community to which the node of the multiple nodes belongs.

公开(公告)号：US20200334495A1

公开(公告)日：2020-10-22

申请号：US16850570

申请日：2020-04-16

Applicant: Google LLC

Inventor： Rami Al-Rfou ,  Dustin Zelle ,  Bryan Perozzi

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20

Abstract: The present disclosure provides computing systems and methods directed to algorithms and the underlying machine learning (ML) models for evaluating similarity between graphs using graph structures and/or attributes. The systems and methods disclosed may provide advantages or improvements for comparing graphs without additional context or input from a person (e.g., the methods are unsupervised). In particular, the systems and methods of the present disclosure can operate to generate respective embeddings for one or more target graphs, where the embedding for each target graph is indicative of a respective similarity of such target graph to each of a set of source graphs, and where a pair of embeddings for a pair of target graphs can be used to assess a similarity between the pair of target graphs.

公开(公告)号：US20200035002A1

公开(公告)日：2020-01-30

申请号：US16523612

申请日：2019-07-26

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Bryan Perozzi

IPC: G06T11/20 ,  G06T9/00

Abstract: The present disclosure is directed to encoding graphs. In particular, the methods and systems of the present disclosure can: receive data describing a first graph; and for each node, of one or more nodes, of the first graph, determine, based at least in part on data describing a second graph, and for each of multiple nodes of the second graph corresponding to the node of the first graph, a representation of a role of the node of the multiple nodes in a community to which the node of the multiple nodes belongs.