GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
This paper presents a new Graph Neural Network type using feature-wise linear modulation.Many standard GNN variants propagate information along the edges of a graph by computing messages based only on the representation of the source of each edge.In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information.Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods.Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature.Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.
new GNN formalism + extensive experiments; showing differences between GGNN/GCN/GAT are smaller than thoughtThe paper proposes a new Graph Neural Network architecture that uses Feature-wise Linear Modulation to condition the source-to-target node message-passing based on the target node representation.