Open Access

ARTICLE

LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

by Shihu Liu, Chunsheng Yang^*, Yingjie Liu

School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, 650504, China

* Corresponding Author: Chunsheng Yang. Email:

(This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)

Intelligent Automation & Soft Computing 2023, 38(3), 203-223. https://doi.org/10.32604/iasc.2023.040818

Received 31 March 2023; Accepted 21 June 2023; Issue published 27 February 2024

Abstract

Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the random walk strategy is improved by integrating the degree information and label information of nodes. Then, the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model. At last, the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification, network reconstruction and link prediction tasks, compared with three benchmark algorithms.

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Keywords

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