Multi-Aspect Edge Device Association Based on Time-Series Dynamic Interaction Networks

The exploration into the evolution of architectural structures in 6G edge computing nodes through time-series-based dynamic interactions offers a compelling investigation within complex systems. In the realm of 6G edge computing, where nodes play a pivotal role, existing approaches primar-ily emphasize the locality of nodes or clustering relationships between networks. Traditional time-series network modeling tends to fixate on local static relationships, overlooking the dynamic interactions between real network nodes. To address this limitation, we present a model based on time-series interactions, specifically crafted for 6G edge computing networks. Our model extends beyond traditional boundaries, facilitating a comparative analysis of network formation across diverse datasets, presenting a valuable methodology for conducting evolutionary studies. The model's validity is demonstrated through evaluations on two real network datasets. Notably, within the 6G edge, a discernible structure emerges when the preference for high-level nodes surpasses a critical threshold.