A link prediction algorithm based on convolutional neural network

Weilun Chen, Huangrong Zou and Yinzuo Zhou

Physica A: Statistical Mechanics and its Applications, 2025, vol. 678, issue C

Abstract: Link prediction is a fundamental problem in complexity science, focusing on forecasting the emergence of new links or identifying missing links within a given network. In this paper, we propose a novel link prediction method named Link Prediction Based on Convolutional Neural Networks (LPCNN). The approach introduces an innovative feature construction technique and leverages the LeNet-LP convolutional neural network architecture, specifically tailored for link prediction tasks. To assess the performance of LPCNN, extensive experiments were conducted on four publicly available datasets. The experimental results demonstrate that the proposed method significantly enhances link prediction accuracy, highlighting its effectiveness and practical applicability.

Keywords: Link prediction; Machine learning; Convolutional neural network; Deep learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437125005746
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:678:y:2025:i:c:s0378437125005746

DOI: 10.1016/j.physa.2025.130922

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().