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Node Classification on Non-Homophilic (Heterophilic) Graphs
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There exists a non-trivial set of graphs where graph-aware models underperform their corresponding graph-agnostic models, e.g. SGC and GCN underperform MLP with 1 layer and 2 layers. Although still controversial, people believe the performance degradation results from heterophily, i.e. there exist much more inter-class edges than inner-class edges. This task aims to evaluate models designed for non-homophilic (heterophilic) datasets.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Edge Directionality Improves Learning on Heterophilic Graphs
Addressing Heterophily in Node Classification with Graph Echo State Networks
GCNH: A Simple Method For Representation Learning On Heterophilous Graphs
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Clenshaw Graph Neural Networks
Revisiting Heterophily For Graph Neural Networks
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Deformable Graph Convolutional Networks
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
New Benchmarks for Learning on Non-Homophilous Graphs
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Beyond Low-frequency Information in Graph Convolutional Networks
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Simple and Deep Graph Convolutional Networks
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Adaptive Universal Generalized PageRank Graph Neural Network
Non-Local Graph Neural Networks
Geom-GCN: Geometric Graph Convolutional Networks
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Simplifying Graph Convolutional Networks
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Graph Attention Networks
Inductive Representation Learning on Large Graphs
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.