ffstruc2vec: Flat, Flexible, and Scalable Learning of Node Representations from Structural Identities

Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific node properties and is widely used in applied domains such as fraud detection and social network analysis. A key challenge is the development of scalable methods that can capture structural characteristics relevant to diverse downstream application tasks. While most existing approaches focus on preserving node proximity, methods that aim to preserve structural properties often lack the flexibility required to represent different types of structural patterns. In this work, we introduce ffstruc2vec, a scalable deep learning framework for learning node embeddings that preserve structural identities, designed for real-world network analysis tasks. The proposed approach employs a flat and efficient architecture that enables flexible modeling of a wide range of structural patterns and facilitates adaptation to different downstream tasks. The experimental results across unsupervised and supervised settings demonstrate that ffstruc2vec achieves competitive and often superior performance compared to existing structure-preserving node embedding methods. In addition, the framework enhances interpretability by quantifying the influence of individual structural patterns on task outcomes, supporting the interpretation of the learned representations. These results indicate that ffstruc2vec offers a flexible and scalable solution for structure-preserving node embedding with practical applicability.