  This paper presents a new Graph Neural Network (GNN) type using feature-wise
linear modulation (FiLM). 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.
