Common information (CI) is ubiquitous in information theory and related areas such as theoretical computer science and discrete probability. However, because there are multiple notions of CI, a unified understanding of the deep interconnections between them is lacking. This monograph seeks to fill this gap by leveraging a small set of mathematical techniques that are applicable across seemingly disparate problems. In Part I, we review the operational tasks and properties associated with Wyner's and Gács-Körner-Witsenhausen's (GKW's) CI. In PartII, we discuss extensions of the former from the perspective of distributed source simulation. This includes the Rényi CI which forms a bridge between Wyner's CI and the exact CI. Via a surprising equivalence between the Rényi CI of order~$\infty$ and the exact CI, we demonstrate the existence of a joint source in which the exact CI strictly exceeds Wyner's CI. Other closely related topics discussed in Part II include the channel synthesis problem and the connection of Wyner's and exact CI to the nonnegative rank of matrices. In Part III, we examine GKW's CI with a more refined lens via the noise stability or NICD problem in which we quantify the agreement probability of extracted bits from a bivariate source. We then extend this to the $k$-user NICD and $q$-stability problems, and discuss various conjectures in information theory and discrete probability, such as the Courtade-Kumar, Li-Médard and Mossell-O'Donnell conjectures. Finally, we consider hypercontractivity and Brascamp-Lieb inequalities, which further generalize noise stability via replacing the Boolean functions therein by nonnnegative functions. The key ideas behind the proofs in Part III can be presented in a pedagogically coherent manner and unified via information-theoretic and Fourier-analytic methods.