  We present a generic and trend-aware curriculum learning approach for graph
neural networks. It extends existing approaches by incorporating sample-level
loss trends to better discriminate easier from harder samples and schedule them
for training. The model effectively integrates textual and structural
information for relation extraction in text graphs. Experimental results show
that the model provides robust estimations of sample difficulty and shows
sizable improvement over the state-of-the-art approaches across several
datasets.
