Measure User Intimacy by Mining Maximum Information Transmission Paths

Lin Guo and Dongliang Zhang

Complexity, 2020, vol. 2020, 1-9

Abstract:

The Internet has become an important carrier of information. Its data contain abundant information about hot events, user relations and attitudes, and so on. Many enterprises use high-impact Internet users to promote products, so it is very important to understand the mechanism of information transmission. Mining social network data can help people analyze the complex and changing relationships between users. The traditional method for doing this is to analyze information such as common interests and common friends, but this data cannot truly describe the degree of intimacy between users. What really connects different users on the Internet is the delivery of information. The algorithm proposed in this paper considers the dynamic characteristics of information transmission, finds maximum transmission paths from information transmission results, and finally calculates the intimacy degrees between users according to all the maximum information transmission paths within a certain period.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/8503/2020/2376451.pdf (application/pdf)
http://downloads.hindawi.com/journals/8503/2020/2376451.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:2376451

DOI: 10.1155/2020/2376451

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().