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

Real-world graphs generally have only one kind of tendency in theirconnections. These connections are either homophily-prone or heterophily-prone.While graphs with homophily-prone edges tend to connect nodes with the sameclass (i.e., intra-class nodes), heterophily-prone edges tend to buildrelationships between nodes with different classes (i.e., inter-class nodes).Existing GNNs only take the original graph during training. The problem withthis approach is that it forgets to take into consideration the ``missing-half"structural information, that is, heterophily-prone topology for homophily-pronegraphs and homophily-prone topology for heterophily-prone graphs. In our paper,we introduce Graph cOmplementAry Learning, namely GOAL, which consists of twocomponents: graph complementation and complemented graph convolution. The firstcomponent finds the missing-half structural information for a given graph tocomplement it. The complemented graph has two sets of graphs including bothhomophily- and heterophily-prone topology. In the latter component, to handlecomplemented graphs, we design a new graph convolution from the perspective ofoptimisation. The experiment results show that GOAL consistently outperformsall baselines in eight real-world datasets.

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs