The Population-Based Structural Health Monitoring (PBSHM) paradigm has recently emerged aiming to enhance data-driven assessment of engineering structures by allowing data to be shared and learning to be transferred between similar structures. In this work, we gear this concept toward automated modal identification of structural systems. Toward modal identification from a PBSHM perspective, we here present a Graph Neural Network (GNN)-based deep learning scheme to identify mode shapes of engineering structures on the basis of monitored (measured) responses. The generation of the training dataset, which includes mode shapes and noise-polluted dynamic responses, relies on availability of an engineering model. Finite element (FE)-based modal and dynamic analyses are first carried out on a population of structures that share certain morphological/typological characteristics but comprise different geometric (size and shape) and material (stiffness) characteristics. The trained model is in a next step fed with dynamic response data from unseen structures and is able to output, in an automated fashion, the corresponding mode shapes. These unseen structures form members of the explored “population” but have not been generated for use within the training set. Moreover, we show that mode shape inference under availability of sparse measurements can be achieved by coupling the GNN with a Feature Propagation operator in what we here term a GNN-OMA approach. A series of numerical experiments are conducted to test the performance of the proposed method. Results show that the proposed model exhibits good accuracy and generalization ability when identifying mode shapes for structures within the same population, rendering its use promising for PBSHM purposes.