Nanjing University of information science Technology, Jiangsu, Nanjing 210044, China.
Xinjiang University, Urumqi 830008, China.
Math Biosci Eng. 2021 Feb 2;18(2):1609-1628. doi: 10.3934/mbe.2021083.
Community detection is a complex and meaningful process, which plays an important role in studying the characteristics of complex networks. In recent years, the discovery and analysis of community structures in complex networks has attracted the attention of many scholars, and many community discovery algorithms have been proposed. Many existing algorithms are only suitable for small-scale data, not for large-scale data, so it is necessary to establish a stable and efficient label propagation algorithm to deal with massive data and complex social networks. In this paper, we propose a novel label propagation algorithm, called WRWPLPA (Parallel Label Propagation Algorithm based on Weight and Random Walk). WRWPLPA proposes a new similarity calculation method combining weights and random walks. It uses weights and similarities to update labels in the process of label propagation, improving the accuracy and stability of community detection. First, weight is calculated by combining the neighborhood index and the position index, and the weight is used to distinguish the importance of the nodes in the network. Then, use random walk strategy to describe the similarity between nodes, and the label of nodes are updated by combining the weight and similarity. Finally, parallel propagation is comprehensively proposed to utilize label probability efficiently. Experiment results on artificial network datasets and real network datasets show that our algorithm has improved accuracy and stability compared with other label propagation algorithms.
社区发现是一个复杂而有意义的过程，它在研究复杂网络的特征方面起着重要的作用。近年来，复杂网络中社区结构的发现和分析引起了许多学者的关注，提出了许多社区发现算法。许多现有的算法仅适用于小规模数据，不适用于大规模数据，因此有必要建立一个稳定而高效的标签传播算法来处理海量数据和复杂社交网络。在本文中，我们提出了一种新的标签传播算法，称为 WRWPLPA（基于权重和随机游走的并行标签传播算法）。WRWPLPA 提出了一种新的相似度计算方法，结合了权重和随机游走。它在标签传播过程中使用权重和相似度来更新标签，提高了社区检测的准确性和稳定性。首先，通过结合邻域指数和位置指数来计算权重，并使用权重来区分网络中节点的重要性。然后，使用随机游走策略来描述节点之间的相似度，并通过结合权重和相似度来更新节点的标签。最后，全面提出并行传播，以有效地利用标签概率。在人工网络数据集和真实网络数据集上的实验结果表明，与其他标签传播算法相比，我们的算法提高了准确性和稳定性。
