Benbatata Sabrina, Saoud Bilal, Shayea Ibraheem, Alsharabi Naif, Alhammadi Abdulraqeb, Alferaidi Ali, Jadi Amr, Daradkeh Yousef Ibrahim
LIM Laboratory, Faculty of Sciences and Applied Sciences, University of Bouira, Bouira, Algeria.
Electrical Engineering Department, Faculty of Sciences and Applied Sciences, University of Bouira, Bouira, Algeria.
PeerJ Comput Sci. 2024 Nov 26;10:e2489. doi: 10.7717/peerj-cs.2489. eCollection 2024.
In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the underlying local network topology. The key innovation of GSeg lies in its encoder-decoder architecture, which is specifically designed to preserve the network's structural properties. The key contributions of GSeg are: (1) a novel graph neural network architecture that effectively captures local and global network structures, and (2) a robust node representation learning approach that achieves superior performance in various network analysis tasks. The methodology employed in our study involves the utilization of a graph neural network framework for the acquisition of node representations. The design leverages the inherent characteristics of nodes and the underlying local network topology. To enhance the architectural framework of encoder- decoder networks, the GSeg model is specifically devised to exhibit a structural resemblance to the SegNet model. The obtained empirical results on multiple benchmark datasets demonstrate that the GSeg outperforms existing state-of-the-art methods in terms of network structure preservation and prediction accuracy for downstream tasks. The proposed technique has potential utility across a range of practical applications in the real world.
本文提出了图分割（GSeg）方法。该解决方案是一种用于网络嵌入的新型图神经网络框架，它利用了节点的固有特性和潜在的局部网络拓扑结构。GSeg的关键创新在于其编码器-解码器架构，该架构专门设计用于保留网络的结构属性。GSeg的关键贡献在于：（1）一种有效捕获局部和全局网络结构的新型图神经网络架构，以及（2）一种在各种网络分析任务中实现卓越性能的强大节点表示学习方法。我们研究中采用的方法涉及利用图神经网络框架来获取节点表示。该设计利用了节点的固有特性和潜在的局部网络拓扑结构。为了增强编码器-解码器网络的架构框架，GSeg模型经过专门设计，使其在结构上与SegNet模型相似。在多个基准数据集上获得的实证结果表明，在下游任务的网络结构保留和预测准确性方面，GSeg优于现有的最先进方法。所提出的技术在现实世界的一系列实际应用中具有潜在的实用价值。
