Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs (HGNNs) have advanced node embeddings and relationship learning for various tasks. However, existing methods often rely on domain-specific predefined meta-paths, which are coarse-grained and focus solely on aspects like node type, limiting their ability to capture complex interactions. We introduce MF2Vec, a model that uses multi-faceted (fine-grained) paths instead of predefined meta-paths. MF2Vec extracts paths via random walks and generates multi-faceted vectors, ignoring predefined schemas. This method learns diverse aspects of nodes and their relationships, constructs a homogeneous network, and creates node embeddings for classification, link prediction, and clustering. Extensive experiments show that MF2Vec outperforms existing methods, offering a more flexible and comprehensive framework for analyzing complex networks. The code is available at https://anonymous.4open.science/r/MF2Vec-6ABC.

翻译：近年来，图神经网络（GNNs）和异质图神经网络（HGNNs）的发展推动了节点嵌入和关系学习在各种任务中的进步。然而，现有方法通常依赖于领域特定的预定义元路径，这些路径是粗粒度的，且仅关注节点类型等方面，限制了其捕捉复杂交互的能力。我们提出了MF2Vec模型，该模型使用多面（细粒度）路径而非预定义的元路径。MF2Vec通过随机游走提取路径，并生成多面向量，忽略预定义的模式。该方法学习节点及其关系的多样化方面，构建同质网络，并创建用于分类、链接预测和聚类的节点嵌入。大量实验表明，MF2Vec优于现有方法，为分析复杂网络提供了一个更灵活和全面的框架。代码可在 https://anonymous.4open.science/r/MF2Vec-6ABC 获取。