The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 10860941 B1

PDF
Full Text
Expired
Date of Patent:
Dec. 08, 2020

Filed:

Mar. 16, 2017
Applicant:

Huawei Technologies Co., Ltd., Shenzhen, Guangdong, CN;

Inventors:

Yang Yang, Beijing, CN;

Wing Ki Leung, Shenzhen, CN;

Jie Tang, Beijing, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2012.01); G06Q 30/00 (2012.01); G06N 5/02 (2006.01); G06F 15/16 (2006.01); G06Q 50/00 (2012.01); G06N 7/00 (2006.01); G06N 20/00 (2019.01); G06F 16/00 (2019.01);
U.S. Cl.
CPC ...
G06N 7/005 (2013.01); G06F 16/00 (2019.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01); G06Q 30/0241 (2013.01); G06Q 50/01 (2013.01);
Abstract

A method for predicting information propagation in a social network includes acquiring target information to be predicted, and acquiring influences of K clusters, where the target information is posted or forwarded by a first user at a first moment, and K is a positive integer; determining a role probability distribution of the first user, and determining a second user who has not propagated the target information, where the role probability distribution of the first user is used to indicate probabilities that the first user belongs separately to the K clusters; and determining, according to the influences of the K clusters and the role probability distribution of the first user, a probability that the second user forwards the target information from the first user. In the embodiments of the present application, propagation of target information in a social network can be predicted by using influences of K clusters.


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