Disclosing Actual Controller based on Equity Knowledge Graph Learning

Qingying Xu, Liang Hong, Mingxuan Shen, Baokun Yi

Abstract: Disclosing Actual Controllers (ACs) of a company has been the basis for financial risk governance. A shareholder in a winning stable coalition, where members make consistent decisions and win in votes, is considered an AC. However, existing methods fail to discover stable coalitions due to the ignorance of various relations other than the shareholding relation among shareholders, such as kinship, subsidiary and so on. Moreover, the above relations form a large-scale equity network, which brings challenges for efficiently identifying winning stable coalitions.We construct an Equity Knowledge Graph (EKG) to represent the semantic and structural information of the equity network. In this paper, we propose an AC disclosure method based on Equity Knowledge Graph Learning (EKGL). Specifically, to discover stable coalitions, EKGL designs a multi-relational aggregation module to aggregate the information of different relations horizontally. Based on the aggregated information, EKGL leverages a metapath-based aggregation module to encode the shareholding structure by capturing different shareholding paths on EKG vertically. To identify winning stable coalitions, we propose a control neural network to simulate the voting process of shareholders. Experiments and a case study on the EKG constructed from real datasets demonstrate that EKGL outperforms baselines by achieving 0.33 improvement in F1 score and reducing time cost.

External IDs:dblp:conf/kdd/XuHSY25