Computer-readable recording medium, information processing apparatus, and data generating method

Patent metadata

Field	Value
Publication number	US-2021192371-A1
Application number	US-202017122621-A
Country	US
Kind code	A1
Filing date	Dec 15, 2020
Priority date	Dec 20, 2019
Publication date	Jun 24, 2021
Grant date	—

Abstract

A shortest path searching unit 121 identifies a first group by a shortest path search conducted from a start point node in a forward direction within a first distance and identifies a second group by another shortest path search conducted from an end point node in a reverse direction within a second distance. A feature graph generating unit 122 generates, when sum of a distance of a first shortest path between the start point node and a first node included in the first group and a distance of a second shortest path between the end point node and a second node included in the second group is not more than a threshold obtained by adding a specific distance to a distance of a shortest path between the start point node and the end point node, a feature graph including the first shortest path and the second shortest path.

First claim

What is claimed is:

1 . A non-transitory computer-readable recording medium having stored therein instructions executable by one or more computer, the instructions comprising:
one or instructions for identifying a first path group by a shortest path search conducted from a start point node in a forward direction within a first distance, the start point node being included in a plurality of nodes in a directed graph; one or instructions for identifying a second path group by another shortest path search conducted from an end point node in a reverse direction within a second distance, the end point node being included in the plurality of nodes; and one or instructions for generating, when sum of a distance of a first shortest path between the start point node and a first node included in the first path group and a distance of a second shortest path between the end point node and a second node included in the second path group is not more than a threshold obtained by adding a specific distance to a distance of a shortest path between the start point node and the end point node, a feature graph including the first shortest path and the second shortest path.

2 . The non-transitory computer-readable recording medium according to claim 1 , wherein each of the first distance and the second distance is equal to the threshold.

3 . The non-transitory computer-readable recording medium according to claim 1 , wherein each of the first distance and the second distance is a value greater than or equal to half of the threshold.

4 . The non-transitory computer-readable recording medium according to claim 1 , the instructions further comprising:
one or instructions for generating a machine learning model by machine learning based on the generated feature graph.

5 . The non-transitory computer-readable recording medium according to claim 4 , the instructions further comprising:
one or instructions for inputting, when receiving designation of a start node and an end node as an estimation target, a feature graph that connects the start node and the end node to the machine learning model; and one or instructions for estimating a relationship between the start node and the end node.

6 . A computing system comprising:
a memory; and a processor coupled to the memory and the processor configured to: identify a first path group by a shortest path search conducted from a start point node in a forward direction within a first distance, the start point node being included in a plurality of nodes in a directed graph; identify a second path group by another shortest path search conducted from an end point node in a reverse direction within a second distance, the end point node being included in the plurality of nodes; and generate, when sum of a distance of a first shortest path between the start point node and a first node included in the first path group and a distance of a second shortest path between the end point node and a second node included in the second path group is not more than a threshold obtained by adding a specific distance to a distance of a shortest path between the start point node and the end point node, a feature graph including the first shortest path and the second shortest path.

7 . The computing system according to claim 6 , wherein each of the first distance and the second distance is equal to the threshold.

8 . The computing system according to claim 6 , wherein each of the first distance and the second distance is a value greater than or equal to half of the threshold.

9 . The computing system according to claim 6 , the processor further configured to generate a machine learning model by machine learning based on the generated feature graph.

10 . The computing system according to claim 9 , the processor further configured to
input, when receiving designation of a start node and an end node as an estimation target, a feature graph that connects the start node and the end node to the machine learning model; and estimate a relationship between the start node and the end node.

11 . A computer-implemented data generating method comprising:
identifying a first path group by a shortest path search conducted from a start point node in a forward direction within a first distance, the start point node being included in a plurality of nodes in a directed graph using a processor; identifying a second path group by another shortest path search conducted from an end point node in a reverse direction within a second distance, the end point node being included in the plurality of nodes using the processor; generating, when sum of a distance of a first shortest path between the start point node and a first node included in the first path group and a distance of a second shortest path between the end point node and a second node included in the second path group is not more than a threshold obtained by adding a specific distance to a distance of a shortest path between the start point node and the end point node, a feature graph including the first shortest path and the second shortest path using the processor.

Assignees

• Fujitsu Ltd

Inventors

• Tago Shinichiro
• Kobayashi Kenichi
• Yoshikawa Hiyori
• Kobayashi Kenji
• Fuji Masaru

Classifications

• G06F16/9024 Primary

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06N5/025

  Extracting rules from data · CPC title