A Network Representation Learning Model Based on Multiple Remodeling of Node Attributes

Wei Zhang, Baoyang Cui, Zhonglin Ye and Zhen Liu ()
Additional contact information
Wei Zhang: School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Baoyang Cui: The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China
Zhonglin Ye: Graduate School of Eegineering, Nagasaki Institute of Applied Science, Nagasaki 851-0123, Japan
Zhen Liu: Graduate School of Eegineering, Nagasaki Institute of Applied Science, Nagasaki 851-0123, Japan

Mathematics, 2023, vol. 11, issue 23, 1-19

Abstract: Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes. Knowledge representation learning aims to learn low-dimensional dense representations of entities and relations from structured knowledge graphs, and most models use the triplets to capture semantic, logical, and topological features between entities and relations. In order to extend the generalization capability of the network representation learning models, this paper proposes a network representation learning algorithm based on multiple remodeling of node attributes named MRNR. The model constructs the knowledge triplets through the textual association relationships between nodes. Meanwhile, a novel co-occurrence word training method has been proposed. Multiple remodeling of node attributes can significantly improve the effectiveness of network representation learning. At the same time, MRNR introduces the attention mechanism to achieve the weight information for key co-occurrence words and triplets, which further models the semantic and topological features between entities and relations, and it makes the network embedding more accurate and has better generalization ability.

Keywords: network representation learning; knowledge modeling; structure modeling; attention; attribute contain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/23/4788/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/23/4788/ (text/html)

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:gam:jmathe:v:11:y:2023:i:23:p:4788-:d:1288838

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().