A flexible framework for multiple-role discovery in real networks

Abstract

Abstract In complex networks, the role of a node is based on the aggregation of structural features and functions. However, in real networks, it has been observed that a single node can have multiple roles. Here, the roles of a node can be defined in a case-by-case manner, depending on the graph data mining task. Consequently, a significant obstacle to achieving multiple-role discovery in real networks is finding the best way to select datasets for pre-labeling. To meet this challenge, this study proposes a flexible framework that extends a single-role discovery method by using domain adversarial learning to discover multiple roles for nodes. Furthermore, we propose a method to assign sub-networks, derived through community extraction methods, to a source network and a validation network as training datasets. Experiments to evaluate accuracy conducted on real networks demonstrate that the proposed method can achieve higher accuracy and more stable results.

Keywords

• Complex network
• Role discovery
• Multi-label classification
• Real network
• Community extraction
• Adversarial learning