Xu Xin, Lu Xinya, Wang Jianan
School of Media Science, Northeast Normal University, Jingye Street 2555, Changchun 130117, China.
School of Journalism, Northeast Normal University, Jingye Street 2555, Changchun 130117, China.
Entropy (Basel). 2025 Mar 20;27(3):322. doi: 10.3390/e27030322.
This paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates them into a cohesive model, addressing limitations in structural feature extraction and neighborhood relationship modeling. DeeWaNA first leverages DeepWalk to capture global structural information and then employs an attention-based weighting mechanism to refine neighborhood relationships through a novel distance metric. Finally, a weighted aggregation operator fuses these representations into a unified low-dimensional space. By bridging the gap between random-walk-based and neural-network-based techniques, our framework enhances representation quality and improves classification accuracy. Extensive evaluations on real-world networks demonstrate that DeeWaNA outperforms four widely used unsupervised network representation learning methods, underscoring its effectiveness and broader applicability.
本文介绍了DeeWaNA，这是一种无监督网络表示学习框架，它将随机游走策略和邻域聚合机制统一起来，以提高节点分类性能。与现有方法将这两种范式分开处理不同，我们的方法将它们集成到一个连贯的模型中，解决了结构特征提取和邻域关系建模方面的局限性。DeeWaNA首先利用DeepWalk来捕获全局结构信息，然后采用基于注意力的加权机制，通过一种新颖的距离度量来细化邻域关系。最后，一个加权聚合算子将这些表示融合到一个统一的低维空间中。通过弥合基于随机游走和基于神经网络的技术之间的差距，我们的框架提高了表示质量并提升了分类准确率。在真实世界网络上的广泛评估表明，DeeWaNA优于四种广泛使用的无监督网络表示学习方法，突出了其有效性和更广泛的适用性。
