Higher-Order Multiple-Feature-based Community Evolution Model with Potential Applications in Criminal Network Investigation

Abstract Dynamic network analysis is a promising research area with a wide range of applications. Criminal network investigation is one of them. People may be interested in using dynamic network analysis to detect criminal communities in a dynamic social network, track the evolution of those communities, identify critical criminal members, or predict links between criminal members and others. One difficulty in applying dynamic network analysis to real-world data is that real-world dynamic networks may vary sparse, which can cause overfitting problem and compromise the performance of the proposed model. Another problem is that each node in a complex real-world network may have multiple features, making it complicated to compute the distance between nodes. We propose a higher-order multiple-feature-based community evolution model (HFCE) to address those two issues. The model uses a higher-order representation of the neighbouring information among nodes to alleviate the sparsity problem. It also introduces first-order similarity regularization to clarify the distance between nodes with multiple features. Experiment results show that the HFCE model outperforms five other popular dynamic network models (ESPRA, AFECT, GenLouvain, ECD and DYNMOGA) in terms of community on the real-world sparse dataset detection and link prediction precision. The HFCE model can also effectively track the evolution of the communities and identify the important nodes in the network over time, which makes it a desirable model in criminal network investigation.