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- W3034452783 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." @default.
- W3034452783 created "2020-06-19" @default.
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- W3034452783 date "2020-07-12" @default.
- W3034452783 modified "2023-09-28" @default.
- W3034452783 title "GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation" @default.
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