@inproceedings{brockschmidt2020gnn-film,
author = {Brockschmidt, Marc},
title = {GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation},
booktitle = {ICML 2020},
year = {2020},
month = {July},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/gnn-film-graph-neural-networks-with-feature-wise-linear-modulation/},
}