  Node classification is an important task to solve in graph-based learning.
Even though a lot of work has been done in this field, imbalance is neglected.
Real-world data is not perfect, and is imbalanced in representations most of
the times. Apart from text and images, data can be represented using graphs,
and thus addressing the imbalance in graphs has become of paramount importance.
In the context of node classification, one class has less examples than others.
Changing data composition is a popular way to address the imbalance in node
classification. This is done by resampling the data to balance the dataset.
However, that can sometimes lead to loss of information or add noise to the
dataset. Therefore, in this work, we implicitly solve the problem by changing
the model loss. Specifically, we study how attention networks can help tackle
imbalance. Moreover, we observe that using a regularizer to assign larger
weights to minority nodes helps to mitigate this imbalance. We achieve State of
the Art results than the existing methods on several standard citation
benchmark datasets.
