  Several distributed frameworks have been developed to scale Graph Neural
Networks (GNNs) on billion-size graphs. On several benchmarks, we observe that
the graph partitions generated by these frameworks have heterogeneous data
distributions and class imbalance, affecting convergence, and resulting in
lower performance than centralized implementations. We holistically address
these challenges and develop techniques that reduce training time and improve
accuracy. We develop an Edge-Weighted partitioning technique to improve the
micro average F1 score (accuracy) by minimizing the total entropy. Furthermore,
we add an asynchronous personalization phase that adapts each compute-host's
model to its local data distribution. We design a class-balanced sampler that
considerably speeds up convergence. We implemented our algorithms on the
DistDGL framework and observed that our training techniques scale much better
than the existing training approach. We achieved a (2-3x) speedup in training
time and 4\% improvement on average in micro-F1 scores on 5 large graph
benchmarks compared to the standard baselines.
