## 5 Conclusions  
Over the last few years, the analysis and modeling of communication networks as complex dynamical systems has attracted high interest, especially in the interdisciplinary research. The 6G network is intrinsically suitable to be modeled as a multidimensional relational system of different sub-networks, represented by various graphs which embed elements that interact in different ways. These elements represent connected heterogeneous things that can share resources, archives, and tasks to provide services. These ecosystems involve devices with constrained resources and computational capabilities and call for novel algorithms and a new characterization for dynamically managing lightweight and simple services as a microservice. It represents a task for a physical edge element of the network, that in a virtual space is connected with other elements, seen as resources able to perform different tasks with the aim at providing an application that is in line with the consumer's requirements. Heterogeneous things and devices (as sensors or hand-held devices), virtualized in edge cloud represent data and computational resources for tasks. In the proposed work, the elements are physical, virtual and concatenated resources. The interest mainly falls on the fact that those systems can be effectively described as complex networks, leveraging on the complex systems theory to engineer, design and model a 6G network. The complex approach for 6G systems will envision distributed artificial intelligence, to implement a fully user-centric network architecture. Introducing complexity through the proposed profiling approach, in 6G network, allows nodes to acquire cognition ability, as the results of processes able to disentangle grades of knowledge on their connectivity and behaviors on multiple different channels by deepening the investigation of structural and social aspects of the network, the collective dynamics of diffusion and competition, and the learning of belonging to various communities. In this paper, we propose a node profiling process that, step-by-step, allows defining of different aspects extracted from a complex network analysis, to shape a profile which embeds macroscopic, microscopic, mesoscopic, dynamical and learning properties. Our proposed profiling framework describes a set of inter-operable abstract classes referred to processes which constitute a cognitive level for nodes and community of nodes in network. We detail metrics and parameters, theory and analytical tools to study the coexistence of various type of interaction among nodes, the multiplex dimension of the network and the interplay of collective dynamics and the mesoscale organization.  
All the authors (M.S., B.A., A.L.C.) have equally contributed to the conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable, the study does not report any data.  
This work was partially supported by the Research Grants: Italian Ministry of University and Research (MIUR)--PON REC 2014--2020 within the project ARS01 01116 \"TALIS-MAN\"--University of Catania.  
The authors declare no conflict of interest.