• Shu Liu, Fujio Toriumi, Mao Nishiguchi, Shohei Usui “A flexible framework for multiple-role discovery in real networks” Applied Network Science Vol.7, 67(09/2022)

    新しい論文がApplied Network Scienceに掲載されました.


    A flexible framework for multiple-role discovery in real networks

    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.