of nodes. Thus, it can be achieved through the introduction of a complex systems approach. The traditional attributes applied to measure and characterize communication networks such as interference, coverage, throughput, robustness and costs are not able to describe dynamics and crucial aspects of future wireless and mobile networks. That is why there is growing interest in studying communication networks from a complex systems perspective, taking into account methodologies and tools built to analyze emerging behaviors, and cooperative and collaborative dynamics, among the elementary units of the system [2; 5; 6]. The upcoming 6G has the ambition to introduce heterogeneous interconnected elements which can dynamically interact with each other as well as with their environment in an unpredictable and unplanned way. Human beings, their devices and, consequently, their behaviors are active elements of the networks, becoming a sort of heterogeneous and aggregated things, with a crucial role in technical systems and in the design of the networking functions, representing the interacting part of a complex socio-technical ecosystem [7; 8]. From the mobile users and their devices (from wearable to hand-held) or the heterogeneous smart devices that form the IoT networks to the plethora of entities of each sub-network that constitute a 6G environment, these can be represented by nodes of a multiplex social network, as shown in Figure 1.  
The multiplex dimension offers a key change in perspective for the structural analysis, and it represents the suitable representation to study the emergence of complex properties of the network [9; 10]. The 6G network is intrinsically suitable to be modeled as the multidimensional relational systems of different sub-networks, represented by various graphs that embed interacting elements in different ways [2].  
Figure 1: Multiplex representation mined from 6G subsystems. This figure shows the schematic representation through the multiplex network of a 6G scenario, including different types of sub-networks, various interactions and the evolutionary dynamics of collective phenomena.  
The designing, modeling and monitoring of the behavior of these systems will be challenges to address. Since we are rapidly moving from a closed to an open and distributed system, and to a completely dynamic topology that will be characterized by a vast heterogeneity, this requires an intelligence at the node level. The node, in a fully user-centric network architecture, plays a key role in content diffusion, learning, computation and organization, and consequently, a new characterization is becoming necessary. For this reason, we propose a node profiling process based on a complex network approach, which embeds, step by step, the knowledge extracted from the introduction of the multiplex dimension, the analysis of the dynamics of collective phenomena as diffusion and competition, applying the epidemics spreading modeling and the evolutionary game theory, from the mesoscale hierarchical organization of the network in communities. We detail the profiling technique through the introduction of metrics and parameters that enables the analysis of three different aspects, structural, collective dynamical and community-based. The paper is organized as follows: in Section 2 we briefly introduce background and methods. In Section 3, we detail the modeling approach to the profiling, in terms of structural, collective and community analysis. After, we show and discuss our findings in Section 4 to conclude in Section 5.