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

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using thegraph structures based on the relational inductive bias (homophily assumption).Though GNNs are believed to outperform NNs in real-world tasks, performanceadvantages of GNNs over graph-agnostic NNs seem not generally satisfactory.Heterophily has been considered as a main cause and numerous works have beenput forward to address it. In this paper, we first show that not all cases ofheterophily are harmful for GNNs with aggregation operation. Then, we proposenew metrics based on a similarity matrix which considers the influence of bothgraph structure and input features on GNNs. The metrics demonstrate advantagesover the commonly used homophily metrics by tests on synthetic graphs. From themetrics and the observations, we find some cases of harmful heterophily can beaddressed by diversification operation. With this fact and knowledge offilterbanks, we propose the Adaptive Channel Mixing (ACM) framework toadaptively exploit aggregation, diversification and identity channels in eachGNN layer to address harmful heterophily. We validate the ACM-augmentedbaselines with 10 real-world node classification tasks. They consistentlyachieve significant performance gain and exceed the state-of-the-art GNNs onmost of the tasks without incurring significant computational burden.

Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?