System and method for graph pattern analysis

• US 9,275,340 B2
• Filed: 12/21/2012
• Issued: 03/01/2016
• Est. Priority Date: 11/30/2007
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Inventors: Patil, Dhanurjay A. S., Jastrebski, Grahame Andrew, Miller, Allison E., Riccomini, Chris
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Lamardo, Viker
• Application Number:
  US13/724,366
• Publication Number:
  US 20130138587A1
• Time in Patent Office: 1,166 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

In some example embodiments, a system and method are provided for graph pattern analysis. In example embodiments, pattern data of a primary network that includes data relating to relationships between entities are received. A secondary network based on the pattern data of the primary network is generated by using an algorithm that processes pattern characteristics extracted from the pattern data. The generated secondary network is provided for further analysis.

Claims

1. A method comprising:

receiving pattern data of a primary network that includes data relating to relationships between entities;

generating a pattern characteristic set based on extracted pattern characteristics extracted from the pattern data;

generating, using a processor of a machine, a secondary network based on the pattern data of the primary network by using an algorithm that processes the pattern characteristics extracted from the pattern data, the generating the secondary network comprises combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and

providing the generated secondary network for further analysis.

2. The method of claim 1, wherein the generating the pattern characteristic set comprises parsing the pattern data using a retrieved parsing grammar to determine the pattern characteristics.

3. The method of claim 1, wherein the generating the secondary network comprises building a data structure using the algorithm, the selected historical data, and the pattern characteristic set.

4. The method of claim 3, further comprising classifying the data structure based on a historical taxonomy.

5. The method of claim 1, wherein the generating the secondary network comprises applying a learning paradigm to historical data, the learning paradigm being a selection from the group consisting of supervised learning, unsupervised learning, and reinforcement learning.

6. The method of claim 1, further comprising storing the secondary network into a data store as a manual pattern, the manual pattern being a stored pattern that closely corresponds to a pattern of the pattern data.

7. The method of claim 1, wherein the data comprises one of commerce transactions or social network data.

8. The method of claim 1, wherein the providing comprises causing the generated secondary network to be graphically displayed to a user.

9. A system comprising:

a receiver to receive pattern data of a primary network that includes data relating to relationships between entities;

a building engine, comprising one or more processors of a machine, to;

generate a pattern characteristic set based on extracted pattern characteristics extracted from the pattern data, and

generate a secondary network based on the pattern data of the primary network by using an algorithm that processes the pattern characteristics extracted from the pattern data, the secondary network generated by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and

a component to provide the generated secondary network for further analysis.

10. A non-transitory machine-readable medium comprising instructions which, when implemented by one or more processors of a machine, cause the machine to perform operations comprising:

receiving pattern data of a primary network that includes data relating to relationships between entities;

generating a pattern characteristic set based on extracted pattern characteristics extracted from the pattern data;

generating a secondary network based on the pattern data of the primary network by using an algorithm that processes the pattern characteristics extracted from the pattern data, the generating the secondary network comprises combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and

providing the generated secondary network for further analysis.

11. The non-transitory machine-readable medium of claim 10, wherein the generating the pattern characteristic set comprises parsing the pattern data using a retrieved parsing grammar to determine the pattern characteristics.

12. The non-transitory machine-readable medium of claim 10, wherein the generating the secondary network comprises building a data structure using the algorithm, the selected historical data, and the pattern characteristic set.

13. The non-transitory machine-readable medium of claim 12, wherein the operations further comprise classifying the data structure based on a historical taxonomy.

14. The non-transitory machine-readable medium of claim 10, wherein the generating the secondary network comprises applying a learning paradigm to historical data, the learning paradigm being a selection from the group consisting of supervised learning, unsupervised learning, and reinforcement learning.

15. The non-transitory machine-readable medium of claim 10, wherein the operations further comprise storing the secondary network into a data store as a manual pattern, the manual pattern being a stored pattern that closely corresponds to a pattern of the pattern data.

16. The non-transitory machine-readable medium of claim 10, wherein the providing comprises causing the generated secondary network to be graphically displayed to a user.