Node Classification On Non Homophilic 11

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1:1 Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	1:1 Accuracy	Paper Title
FAGCN	46.07 ± 2.11	Beyond Low-frequency Information in Graph Convolutional Networks
NLMLP	50.7 ± 2.2	Non-Local Graph Neural Networks
ACM-GCN+	74.47 ± 1.84	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	69.14 ± 1.91	Revisiting Heterophily For Graph Neural Networks
Diag-NSD	68.68 ± 1.73	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
O(d)-NSD	68.04 ± 1.58	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACMII-GCN	68.46 ± 1.7	Revisiting Heterophily For Graph Neural Networks
Dir-GNN	79.71±1.26	Edge Directionality Improves Learning on Heterophilic Graphs
ACM-GCN++	74.41 ± 1.49	Revisiting Heterophily For Graph Neural Networks
GPRGCN	62.59 ± 2.04	Adaptive Universal Generalized PageRank Graph Neural Network
ScaleNet	80.1±1.5	Scale Invariance of Graph Neural Networks
Gen-NSD	67.93 ± 1.58	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACMII-GCN++	74.76 ± 2.2	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	74.56 ± 2.08	Revisiting Heterophily For Graph Neural Networks
NLGAT	65.7 ± 1.4	Non-Local Graph Neural Networks
WRGAT	65.24 ± 0.87	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
GESN	77.05 ± 1.24	Addressing Heterophily in Node Classification with Graph Echo State Networks
MixHop	60.50 ± 2.53	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
GloGNN++	71.21 ± 1.84	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
H2GCN	60.11 ± 2.15	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

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