Wang Xiaojie, Zhao Haijun, Chen Huayue
School of Computer Science, China West Normal University, Nanchong 637002, China.
Sensors (Basel). 2023 Jul 19;23(14):6527. doi: 10.3390/s23146527.
Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially applied to word embedding, is left insufficiently explored. To compensate for the shortcoming, we analyze the difference between word embedding and graph embedding and reveal the principle of graph representation learning through a case study to explain the essential idea of graph embedding intuitively. Through the case study and in-depth understanding of graph embeddings, we propose Graph Skip-gram, an extension of the Skip-gram model using graph structure information. Graph Skip-gram can be combined with a variety of algorithms for excellent adaptability. Inspired by word embeddings in natural language processing, we design a novel feature fusion algorithm to fuse node vectors based on node vector similarity. We fully articulate the ideas of our approach on a small network and provide extensive experimental comparisons, including multiple classification tasks and link prediction tasks, demonstrating that our proposed approach is more applicable to graph representation learning.
近年来，将Skip-gram应用于图表示学习已成为一个被广泛研究的课题。先前的工作通常侧重于Skip-gram模型的迁移应用，而在图表示学习中，最初应用于词嵌入的Skip-gram却未得到充分探索。为弥补这一不足，我们分析了词嵌入与图嵌入之间的差异，并通过案例研究揭示图表示学习的原理，以直观地解释图嵌入的基本思想。通过案例研究和对图嵌入的深入理解，我们提出了Graph Skip-gram，这是一种利用图结构信息对Skip-gram模型的扩展。Graph Skip-gram可以与多种算法相结合，具有出色的适应性。受自然语言处理中词嵌入的启发，我们设计了一种新颖的特征融合算法，基于节点向量相似度融合节点向量。我们在一个小型网络上充分阐述了我们方法的思路，并提供了广泛的实验比较，包括多个分类任务和链接预测任务，证明我们提出的方法更适用于图表示学习。
