Representation Learning on Graphs with Jumping Knowledge Networks

2018

Citations in the corpus

• Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, background): “However, since our goal is to embed each graph as a single point in the embedding space that preserves graph-graph proximity, the graph embedding generation model should capture structural difference at multiple scales (Xu et al., 2018) and be adaptive to different graph proximity metrics.”
• Scaling Graph Neural Networks with Approximate PageRank (2020, background): “2 Second, it has been shown [33, 52] that naively stacking multiple layers may suffer from over-smoothing that can reduce predictive performance.”