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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Graph Neural Networks (GNNs) have achieved remarkable performance in modelinggraphs for various applications. However, most existing GNNs assume the graphsexhibit strong homophily in node labels, i.e., nodes with similar labels areconnected in the graphs. They fail to generalize to heterophilic graphs wherelinked nodes may have dissimilar labels and attributes. Therefore, in thispaper, we investigate a novel framework that performs well on graphs witheither homophily or heterophily. More specifically, we propose a label-wisemessage passing mechanism to avoid the negative effects caused by aggregatingdissimilar node representations and preserve the heterophilic contexts forrepresentation learning. We further propose a bi-level optimization method toautomatically select the model for graphs with homophily/heterophily.Theoretical analysis and extensive experiments demonstrate the effectiveness ofour proposed framework for node classification on both homophilic andheterophilic graphs.

Label-Wise Graph Convolutional Network for Heterophilic Graphs