Direction of arrival (DOA) estimation plays a pivotal role in array processing and radar technology, with ongoing dis- cussions centered around the utilization of mixed-resolution data incorporating both analog and 1-bit quantized measurements. Investigating the impact of varying the number of low-resolution measurements on different analytic algorithms aims to uncover methods for effectively leveraging both low and high-resolution sensors, enhancing DOA estimation in mixed-resolution array cases. Concurrently, DOA estimation, supported by graph signal processing, introduces innovative methodologies for discerning arrival directions across various applications. This work delves into the utilization of graph-based approaches to address this challenge, juxtaposing them against established methods such as the MUSIC algorithm. Specifically, a multi-objective DOA methodology is explored, leveraging a fully connected graph to emulate sensor connectivity. Simulation outcomes validate the efficacy of graph-based solutions, closely aligning with conven- tional approaches and highlighting their potential in resolving this pertinent issue.