Graph Learning with 1D Convolutions on Random Walks

17 Feb 2021 β€’ Jan Toenshoff β€’ Martin Ritzert β€’ Hinrikus Wolf β€’ Martin Grohe

We propose CRaWl (CNNs for Random Walks), a novel neural network architecture for graph learning. It is based on processing sequences of small subgraphs induced by random walks with standard 1D CNNs... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification REDDIT-B CRaWl Accuracy 93.15 # 1
Graph Regression ZINC CRaWl MAE 0.101 # 2
Graph Regression ZINC CRaWl+VN MAE 0.088 # 1
Graph Regression ZINC-500k CRaWl+VN MAE 0.088 # 1
Graph Regression ZINC-500k CRaWl MAE 0.101 # 2

Methods used in the Paper


METHOD TYPE
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