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：US20230138371A1

公开(公告)日：2023-05-04

申请号：US17912233

申请日：2020-09-01

Applicant: Google LLC

Inventor： Farhana Bandukwala ,  Georg Matthias Goerg ,  Samuel Paul Ruth ,  Jonathan Jesse Halcrow

IPC: G06F11/36

Abstract: A method of identifying a contributing cause of an anomaly including receiving a set of timeseries data representing metric values over time, wherein the timeseries data has at least two dimensions, for each of two or more of the timestamps in the set of timeseries data, generating a first and second graph representing (i) the metric values at that timestamp, (ii) the at least two dimensions at that timestamp, and (iii) associations between the metric values at that timestamp, analyzing the first and second graphs associated with each timestamp to identify a particular timestamp including an anomaly, and analyzing the first and second graphs associated with the identified particular timestamp to identify a node that contributed in causing the anomaly.