Augmented Complex Networks - Trustworthy Analysis

Complex networks analysis have been shown to be useful for gaining insights in a wide variety of applications domains such as social interactions, ecology, biological, healthcare, safety and security, and many more. These applications have motivated the extension of network models to richer representations and augmented complex network models, such as feature-rich and higher-order network models. Examples include multiplex networks, multilayer networks, temporal networks attributed networks and network on networks. Machine learning approaches are being extensively used for obtaining insights into/using these models and for mining knowledge from these models and for a variety of tasks including node classification and role identification, link prediction, community detection, anomaly detection and much more. These tasks are generally required to enable and support critical decisions and should so be largely trustworthy. XAI approaches should be adapted to the networked context, and network analysis can also contribute to devise explainability- oriented features in the targeted applications areas. This conference aims to bridge the gap between these areas by exploring the use of complex network analysis and explainability in the following featured application areas: Complex networks in safety and security, Biological and ecological networks and Online Social Networks.