Traditional link recommendation algorithms in social networks often promote popular nodes, leading to societal polarization and the marginalization of minority voices. Conversely, several studies have notably emphasized fairness and diversity in recommendation algorithms. However, these studies tend not to prioritize the visibility of minority groups in their design. Instead, they focus on optimizing other objectives related to fairness and diversity, which may not necessarily align with enhancing minority visibility. Our study investigates the impact of fairness and diversity-aware algorithms on the visibility of structural minorities in social networks. We assess several of these algorithms, noting their varied effects, which range from indifference to potential backlash from the majority. Additionally, we explore the influence of these algorithms on network cohesion and popularity bias. A key contribution of our work is the introduction of MinWalk, a novel algorithm designed to enhance the visibility of minorities in a balanced manner. Our approach to algorithmic fairness specifically addresses the challenges associated with minority representation.