Topological Identification of Agent Status in Information Contagions: Application to Financial Markets

Cascade models serve as effective tools for understanding the propagation of information and diseases within social networks. Nevertheless, their applicability becomes constrained when the states of the agents (nodes) are hidden and can only be inferred through indirect observations or symptoms. This study proposes a Mapper-based strategy to infer the status of agents within a hidden information cascade model using expert knowledge. To verify and demonstrate the method we identify agents who are likely to take advantage of information obtained from an inside information network. We do this using data on insider networks and stock market transactions. Recognizing the sensitive nature of allegations of insider trading, we design a conservative approach to minimize false positives, ensuring that innocent agents are not wrongfully implicated. The Mapper-based results systematically outperform other methods, such as clustering and unsupervised anomaly detection, on synthetic data. We also apply the method to empirical data and verify the results using a statistical validation method based on persistence homology. Our findings highlight that the proposed Mapper-based technique successfully identifies a subpopulation of opportunistic agents within the information cascades. The adaptability of this method to diverse data types and sizes is demonstrated, with potential for tailoring for specific applications.