Event cameras generate data based on the amount of motion present in the captured scene, making them attractive sensors for solving ob- ject tracking tasks. In this paper, we present a framework for tracking humans using a single event camera which consists of three compo- nents. First, we train a Graph Neural Network (GNN) to recognize a person within the stream of events. Batches of events are represented as spatio-temporal graphs in order to preserve the sparse nature of events and retain their high temporal resolution. Subsequently, the person is localized in a weakly-supervised manner by adopting the well established method of Class Activation Maps (CAM) for our graph-based classification model. Our approach does not require the ground truth position of humans during training. Finally, a Kalman filter is deployed for tracking, which uses the predicted bounding box surrounding the human as measurement. We demonstrate that our approach achieves robust tracking results on test sequences from the Gait3 database, paving the way for further privacy-preserving methods in event-based human tracking. Code, pre-trained models and datasets of our research are publicly available