Structures built upon great quantities of networked entities, such as computer networks and social networks, have an undeniable central role in our everyday life. The need to study these complex real-world topologies, together with the growing ability to carry out these studies thanks to technological advances, recently made the use of complex network models pervasive in many disciplines such as computer science, physics, social science, as well as in interdisciplinary research environments. A special focus among others can be made on cyber-physical networks, which refers to multi-layer networks depicting at least two related sets of interactions of different types; some occur in the physical world while others take place in the cyberspace. Social systems, in our moderns times, with the ubiquitous use of on-line social media combined with the ubiquitous use of connected devices (smart phones, connected objects) are one obvious example of targeted systems. Modern critical infra-structure systems such as power grids, public transportation systems, financial networks, the World Wide Web and the Internet are further additional examples. In all these different systems, cyber and physical layers evolve at different paces but the evolution of one layer is tightly influencing the dynamic of and on other layers. We use the term Networks on Networks to designate the fact that even if each network (or layer) can be studied separately as frequently done today, our goal is to study the mutual influence among different layers of the same cyber-physical network. The definition of networks on network has been kept intentionally wide and flexible, with the aim to gather under a common denomination a series of network models exhibiting different structures that were introduced for different needs; while these exhibit specific foci, at the same time they also show some common characteristics and can lead to similar problems. Moreover, the research community also needs an insight into how correctly handling such structures can lead to the definition of network modeling, analysis and mining methods that are able to address classic tasks (e.g., community detection, link prediction, information propagation, and so on), improving upon classical models in terms of results quality, while also considering the impact on their efficiency and scalability. In addition, the concepts of network on networks further provides also forms and novel problem areas which might not be adequately addressed by existing graph models, thus requiring novel approaches and solutions.
The aim of this special session, is to get an insight into the current status of research in network on networks modeling, analysis and mining, showing how modeling information in Networks on Networks can make it possible to focus on domains and research questions that have not been deeply investigated so far and to improve solutions to classic tasks. We encourage contributions on methods and techniques that are both domain-specific but also transversal to different application domains. We will consider the two main aspects of network analysis: modeling and knowledge discovery. The session should point out this differentiation, and enforce the interaction between researchers from different domains. In particular, we solicit contributions that aim to focus on the analysis of networks on networks, addressing important principles, methods, tools and future research directions in this emerging field. The special session will increase the visibility of the above research themes, and will also bridge research tasks from different fields of data science. Even though the focus is on computer science, the themes of the special session also encourage interdisciplinary discussion about topics touching different fields such as physics, social science and humanities.