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NENN: Incorporate Node and Edge Features in Graph Neural Networks

EasyChair Preprint no. 3486

7 pagesDate: May 25, 2020


Graph neural networks (GNNs) have attracted an increasing attention in recent years. However, most existing state-of-the-art graph learning methods only focus on node features and largely ignore the edge features that contain rich information about graphs. In this paper, we propose a novel model to incorporate Node and Edge features in graph Neural Networks (NENN) based on a hierarchical dual-level attention mechanism. Specifically, the node-level attention layer and edge-level attention layer are alternately stacked to learn the importance of the node based neighbors and edge based neighbors for each node and edge. Leveraging the proposed NENN, the node and edge embeddings can be mutually reinforced. Extensive experiments on academic citation and molecular networks have verified the effectiveness of our proposed graph embedding model.

Keyphrases: Attention Mechanism, edge features, Graph Neural Networks

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Yulei Yang and Dongsheng Li},
  title = {NENN: Incorporate Node and Edge Features in Graph Neural Networks},
  howpublished = {EasyChair Preprint no. 3486},

  year = {EasyChair, 2020}}
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