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

These embeddings can be leveraged in various downstream tasks, including node classification, link prediction, clustering, exploratory data analysis, and network visualization. The method has found broad application across diverse domains, such as fraud detection in financial networks (van Belle et al. 2023), friendship recommendation and bot detection in social networks (Saxena et al. 2022; Dehghan et al. 2023), knowledge discovery in knowledge graphs (Egami et al. 2023; Liu et al. 2023), analysis of biological networks (Jiang et al. 2021; Pasquier et al. 2023), and fake review detection on online platforms (Zaki et al. 2024). A key challenge in Node Embedding is developing a scalable method for preserving the structural properties of nodes suitable for the required structural patterns of a downstream application task. The type of structural patterns in which a node is embedded within the graph can vary depending on the role or function of the node in a specific application task. For instance, fraudulent activities such as money laundering can be embedded in particular money flow patterns among illicit entities, resulting in characteristic structural patterns within the financial transaction network, such as suspicious cyclic transaction chains (Granados Vargas 2022). These structural patterns differ significantly from those observed in social networks, where specific roles such as bridge and core nodes define the network's connectivity and influence (Huang et al. 2014). As Node Embedding methods cannot preserve all types of structural patterns simultaneously, they must align with the requirements of a specific application task when defining types of structural identities.