Random walks on graphs are a fundamental concept in graph theory and play a crucial role in solving a wide range of theoretical and applied problems in discrete math, probability, theoretical computer science, network science, and machine learning. The connection between Markov chains on graphs and their geometric and topological structures is the main reason why such a wide range of theoretical and practical applications exist. Graph connectedness ensures irreducibility of a Markov chain. The convergence rate to the stationary distribution is determined by the spectrum of the graph Laplacian which is associated with lower bounds on graph curvature. Furthermore, walks on graphs are used to infer structural properties of underlying manifolds in data analysis and manifold learning. However, an important question remains: can similar connections be established between Markov chains on simplicial complexes and the topology, geometry, and spectral properties of complexes? Additionally, can we gain topological, geometric, or analytic information about a manifold by defining appropriate Markov chains on its triangulations? These questions are not only theoretically important but answers to them provide powerful tools for the analysis of complex networks that go beyond the analysis of pairwise interactions. In this paper, we provide an integrated overview of the existing results on random walks on simplicial complexes, using the novel perspective of signed graphs. This perspective sheds light on previously unknown aspects such as irreducibility conditions. We show that while up-walks on higher dimensional simplexes can never be irreducible, the down walks become irreducible if and only if the complex is orientable. We believe that this new integrated perspective can be extended beyond discrete structures and enables exploration of classical problems for triangulable manifolds.