Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Saghar Bagheri Gene Cheung Tim Eadie
In agronomics, predicting crop yield at a per field/county granularity is
important for farmers to minimize uncertainty and plan seeding for the next
crop cycle. While state-of-the-art prediction techniques employ graph
convolutional nets (GCN) to predict future crop yields given relevant features
and crop yields of previous years, a dense underlying graph kernel requires
long training and execution time. In this paper, we propose a graph
sparsification method based on the Fiedler number to remove edges from a
complete graph kernel, in order to lower the complexity of GCN
training/execution. Specifically, we first show that greedily removing an edge
at a time that induces the minimal change in the second eigenvalue leads to a
sparse graph with good GCN performance. We then propose a fast method to choose
an edge for removal per iteration based on an eigenvalue perturbation theorem.
Experiments show that our Fiedler-based method produces a sparse graph with
good GCN performance compared to other graph sparsification schemes in crop
yield prediction.