Monitoring Sparse and Attributed Network Streams with MultiLevel and Dynamic Structures

• Published in: Mathematics (Basel) Vol. 10; no. 23; p. 4483
• Main Authors: Mostafapour, Mostafa, Movahedi Sobhani, Farzad, Saghaei, Abbas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Analysis; Case studies; Change detection; Contingency; Control charts; Electronic surveillance; Extended Kalman filter; Food science; Generalized linear models; interbank multilevel network; Logit models; Methods; Monitoring; Multilevel; multilevel networks; Neural networks; Nodes; Parameters; Performance evaluation; Regression analysis; Securities markets; Social networks; State space models; Statistical analysis; Statistical models; Statistical process control; Stock exchanges; Streaming media; Streams; temporal monitoring; zero-inflated generalized linear mixed models; Europe; United Kingdom > UK
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10234483

In this study, we create a new monitoring system for change detection in sparse attributed network streams with multilevel or nested dynamic structures. To achieve this, we hypothesize that the contingency of establishing an edge between two network nodes at time t depends on the properties of the network edges, network nodes, groups, or categories. Then, we estimate the model parameters using the expressed logit model. The model parameters are developed using the state-space model to achieve a dynamic state in the system. The extended Kalman filter (EKF) updates state-space parameters and predicts upcoming networks. Predicted residuals are tracked using statistical process control charts to identify changes in the underlying mechanism of edge generation. This research makes a methodological contribution by combining zero-inflated generalized linear mixed models (ZI-GLMMs) with the state-space model to monitor changes in the sequences of sparse, attributed, and weighted multilevel networks by applying control charts. The proposed model is compared to previous models to evaluate performance by implementing three scenarios. The results show that the model is faster at detecting the first change. Finally, using real e-MID data, we measured the model’s performance in detecting real data changes. The findings suggest that the proposed model could predict a crisis in advance of significant European Central Bank statements and events.