Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity
By Eliav Buchnik, Edith Cohen. 2018
13 citations recorded by the source corpus.
Graph-based semi-supervised learning (SSL) algorithms predict labels for all nodes based on provided labels of a small set of seed nodes. Classic methods capture the graph structure through some underlying diffusion process that propagates through the graph edges. Spectral diffusion, which includes personalized page rank and label propagation, propagates through random walks. Social diffusion propagates through shortest paths. These diffusions are linear in the sense of not distinguishing between contributions of few "strong" relations or many "weak" relations.
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- Stephan Günnemann
- Aleksandar Bojchevski
- Johannes Klicpera
- Daniel Zügner
- Matthias Fey
- Oleksandr Shchur
- William L. Hamilton
- Xiyuan Wang
Citations in the corpus
- Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018, methodology): “…models: GCN (Kipf & Welling, 2017), network of GCNs (N-GCN) (Abu-El-Haija et al., 2018a), graph attention networks (GAT) (Veliˇckovi´c et al., 2018), bootstrapped feature propagation (bt. FP) (Buchnik & Cohen, 2018) and jumping knowledge networks with concatenation (JK) (Xu et al., 2018).”