Wei Shaohan
School of Computing and Information Science, Fuzhou Institute of Technology, Fuzhou, Fujian, China.
PeerJ Comput Sci. 2024 Nov 26;10:e2560. doi: 10.7717/peerj-cs.2560. eCollection 2024.
Graph embedding has gained significant popularity due to its ability to represent large-scale graph data by mapping nodes to a low-dimensional space. However, most of the existing research in this field has focused on transductive learning, where fixed node embeddings are generated by training the entire graph. This approach is not well-suited for temporal graphs that undergo continuous changes with the addition of new nodes and interactions. To address this limitation, we propose an inductive temporal graph embedding method called MIAN (Multi-angle Information Aggregation Network). The key focus of MIAN is to design an aggregation function that combines multi-angle information for generating node embeddings. Specifically, we divide the information into different angles, including neighborhood, temporal, and environment. Each angle of information is modeled and mined independently, and then fed into an improved gated recuttent unit (GRU) module to effectively combine them. To assess the performance of MIAN, we conduct extensive experiments on various real-world datasets and compare its results with several state-of-the-art baseline methods across diverse tasks. The experimental findings demonstrate that MIAN outperforms these methods.
图嵌入因其能够通过将节点映射到低维空间来表示大规模图数据而广受欢迎。然而，该领域的大多数现有研究都集中在转导学习上，即在训练整个图时生成固定的节点嵌入。这种方法不太适合随着新节点和交互的添加而不断变化的时态图。为了解决这一局限性，我们提出了一种归纳时态图嵌入方法，称为MIAN（多角度信息聚合网络）。MIAN的关键重点是设计一种聚合函数，该函数结合多角度信息来生成节点嵌入。具体而言，我们将信息分为不同角度，包括邻域、时间和环境。每个信息角度都独立建模和挖掘，然后输入到一个改进的门控循环单元（GRU）模块中，以有效地将它们组合起来。为了评估MIAN的性能，我们在各种真实世界数据集上进行了广泛的实验，并将其结果与多种不同任务中的几个最先进的基线方法进行了比较。实验结果表明，MIAN优于这些方法。
