<p>Physics has demonstrated its utility in addressing real-world problems, including community detection in complex networks. When a complex network is modeled as a graph, community detection involves identifying groups of nodes that are more densely interconnected with each other than with the rest of the graph. This paper presents an up-to-date survey of physics-inspired algorithms for community detection, highlighting how principles and equations from various physical phenomena can inspire innovative solutions to solve this challenging task. By modeling complex systems and their interrelationships through physics-inspired methods, this survey underscores the significant contributions of physics to advancing community detection techniques.</p>