OpenCodePapers

node-classification-on-non-homophilic-15

Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs
Results over time
Click legend items to toggle metrics. Hover points for model names.
Leaderboard
Click a sortable column header to sort.
PaperCode1:1 AccuracyModelNameReleaseDate
Addressing Heterophily in Node Classification with Graph Echo State Networks✓ Link68.34 ± 0.86GESN2023-05-14
Clenshaw Graph Neural Networks✓ Link66.56 ± 0.28ClenshawGCN2022-10-29
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily✓ Link66.34 ± 0.29GloGNN++2022-05-15
Revisiting Heterophily For Graph Neural Networks✓ Link66.24 ± 0.24ACM-GCN+2022-10-14
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily✓ Link66.19 ± 0.29GloGNN2022-05-15
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link66.06 ± 0.19LINKX2021-10-27
Revisiting Heterophily For Graph Neural Networks✓ Link65.943 ± 0.284ACM-GCN++2022-10-14
Revisiting Heterophily For Graph Neural Networks✓ Link65.92 ± 0.14ACMII-GCN++2022-10-14
Revisiting Heterophily For Graph Neural Networks✓ Link65.838 ± 0.153ACMII-GCN+2022-10-14
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing✓ Link65.64 ± 0.27MixHop2019-04-30
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks✓ Link65.02 ± 0.16C&S 2-hop2020-10-27
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks✓ Link64.86 ± 0.27C&S 1-hop 2020-10-27
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link64.85 ± 0.21LINK 2021-10-27
Revisiting Heterophily For Graph Neural Networks✓ Link63.92 ± 0.19ACM-GCN2022-10-14
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link63.88 ± 0.24L Prop 2-hop2021-10-27
Revisiting Heterophily For Graph Neural Networks✓ Link63.73 ± 0.13ACMII-GCN2022-10-14
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link63.45 ± 0.22GCNJK2021-10-27
Simple and Deep Graph Convolutional Networks✓ Link63.39 ± 0.61GCNII2020-07-04
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link62.77 ± 0.24L Prop 1-hop2021-10-27
Semi-Supervised Classification with Graph Convolutional Networks✓ Link62.18 ± 0.26GCN2016-09-09
Adaptive Universal Generalized PageRank Graph Neural Network✓ Link61.89 ± 0.29GPRGCN2020-06-14
Predict then Propagate: Graph Neural Networks meet Personalized PageRank✓ Link60.97 ± 0.10APPNP2018-10-14
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link60.92 ± 0.07MLP2021-10-27
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods✓ Link59.98 ± 2.87GATJK2021-10-27
Simplifying Graph Convolutional Networks✓ Link59.94 ± 0.21SGC 2-hop2019-02-19
Simplifying Graph Convolutional Networks✓ Link58.97 ± 0.19SGC 1-hop2019-02-19
Check out how to contribute new results.