RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
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by
Anahita Iravanizad, Edgar Ivan Sanchez Medina, Martin Stoll
2021
Abstract
In recent years, graph neural networks (GNNs) have gained increasing
popularity and have shown very promising results for data that are represented
by graphs. The majority of GNN architectures are designed based on developing
new convolutional and/or pooling layers that better extract the hidden and
deeper representations of the graphs to be used for different prediction tasks.
The inputs to these layers are mainly the three default descriptors of a graph,
node features (X), adjacency matrix (A), and edge features (W) (if
available). To provide a more enriched input to the network, we propose a
random walk data processing of the graphs based on three selected lengths.
Namely, (regular) walks of length 1 and 2, and a fractional walk of length
γ∈ (0,1), in order to capture the different local and global dynamics
on the graphs. We also calculate the stationary distribution of each random
walk, which is then used as a scaling factor for the initial node features
(X). This way, for each graph, the network receives multiple adjacency
matrices along with their individual weighting for the node features. We test
our method on various molecular datasets by passing the processed node features
to the network in order to perform several classification and regression tasks.
Interestingly, our method, not using edge features which are heavily exploited
in molecular graph learning, let a shallow network outperform well known deep
GNNs.
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