The rapid integration of Large Language Models (LLMs) into everyday applications raises critical questions about their group interactions, consensus formation, and potential to mimic human-like behavior. Although initial research has explored the evolution of opinions within LLM populations, these efforts often rely on simplistic network assumptions, such as uniform connections among agents, thereby overlooking the influence of more realistic network topologies. This paper introduces a framework for examining opinion dynamics among LLM agents within various network structures. We perform several multi-model simulations on network topologies with known locally assortative/disassortative mixing patterns. We find that convergence is quicker in mostly-disassortative networks compared to networks with no mixing biases. However, the joint effect of assortative and disassortative patterns leads to slower/no convergence.