Many networks include both allies and adversaries, excitations and inhibitions, agreements and disagreements. Signed networks offer a formal way to represent these interactions, but negative edges introduce many challenges in standard notions like communities, distances, and centralities. In this talk, I will present the most common concepts and methodologies used in the study of signed networks, with a focus on structural balance and mesoscale structures. I will then introduce the Gremban expansion, a method that transforms signed networks into unsigned ones without information loss. This framework enables a principled separation between communities and factions, addressing the limitations of existing algorithms (which typically recover only factions).