Graph convolutional layers are the core of GCNNs and are used to collect information from neighboring nodes using a message-passing framework. The characterization of the neighborhood is performed through a radius cutoff, which is a hyperparameter of the GCNN model. Each node representing an atom in the structure is connected through an edge to all neighboring nodes within a distance shorter than the radius cutoff, whereas interactions with atoms with a distance longer than the radius-cutoff are disregarded. Even though metallic bonds are characterized by a "sea" of delocalized valence electrons, the cohesive bonding strength nevertheless is strongly determined by the immediate interactions with nearby atoms. Thus forcing nodes in the graph to be locally connected to all neighbouring atoms within a sphere determined by the radius cutoff accurately mimics the physics of metallic alloys.