Link prediction based on the naive Bayes with high-order clustering structure of node

Xiaoqiang Li

doi:10.1117/12.2639914

19 October 2022 Link prediction based on the naive Bayes with high-order clustering structure of node

Xiaoqiang Li

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 1229458 (2022) https://doi.org/10.1117/12.2639914
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

Link prediction, a significant branch of complex networks, has attracted the attention of a growing number of scholars. It is an important tool in data mining. It is used to predict possible future links in the network or links that have not been observed yet. It can also be used to identify spurious links. The Local Naïve Bayes Model accurately distinguishes the contribution of different common neighbor nodes to the formation of the target link, but it only considers the contribution of common neighbors. A large number of networks have higher-order characteristics, and higher-order structures capture as much information about the network. In the work, we proposed a novel method of link prediction based on Naive Bayes with High-Order clustering structure (NBHO) of node. NBHO not only overcomes the shortcomings of the co-neighbor (common neighbor) similarity index that each co-neighbor of two nodes contributes equally to the likelihood of the connection, but also makes use of a higher order clustering structure. High-order structure plays an important role in the evolution of the network. Compared with the traditional method, this framework can provide more accurate predictions. Obviously there is a conclusion that the higher order structure significantly improves the accuracy of the predictions.

Citation Download Citation

Xiaoqiang Li "Link prediction based on the naive Bayes with high-order clustering structure of node", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 1229458 (19 October 2022); https://doi.org/10.1117/12.2639914

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Probability theory

Social networks

Yeast

Data modeling

Data mining

Detection and tracking algorithms

Information science

Show All Keywords

Keywords/Phrases

Search In:

Publication Years