In this study, we compared the welding of branched and linear polymers utilizing both CGMD simulations and experiments. First, the CGMD simulations demonstrated that the growth of interdiffusion layer follows a power law. The exponent decreases as grafting density increases, indicating that sidechains impede polymer interdiffusion. Second, both simulations and experiments indicated that the interfaces between polymers with higher grafting densities saturate the interdigitation threshold and achieve rupture strength parity to the bulk with less welding times, consistent with our hypothesis that the dilution of dynamic constraints enhances interlayer welding. This behavior arises because linear polymers in welding rely on backbone entanglements, requiring more time for their longer backbones to interlock with neighboring backbones. In contrast, bottlebrush polymers weld through sidechain interactions, which occur more rapidly due to their shorter length. However, it should be noted that entanglements are not the only topological constraints in linear systems. Crystals play a confounding role but can't really be captured at these length scales in CGMD faithfully. Additionally, both CGMD simulations and experiments revealed a transition from brittle to elastomeric rupture behavior with increasing grafting density. Finally, during the deformation, all polymers showed an increase in Rg parallel to the deformation direction. The increasing slope is positively correlated with grafting density, indicating that the sidechains hinder orientation relaxation. This study provides valuable insights into the mechanisms underlying the welding of branched polymers, highlighting the interplay between polymer topology, interdiffusion dynamics, and mechanical performance. Our findings pave the way for designing high-performance polymers for additive manufacturing and other applications requiring tunable mechanical properties. Future work could consider incorporate additional factors, such as backbone/sidechain phase separation and crystallization, into our models.