English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 ofthe 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 nodeclassification. This is done by resampling the data to balance the dataset.However, that can sometimes lead to loss of information or add noise to thedataset. Therefore, in this work, we implicitly solve the problem by changingthe model loss. Specifically, we study how attention networks can help tackleimbalance. Moreover, we observe that using a regularizer to assign largerweights to minority nodes helps to mitigate this imbalance. We achieve State ofthe Art results than the existing methods on several standard citationbenchmark datasets.

ReGrAt: Regularization in Graphs using Attention to handle class imbalance