Papers with Code 아카이브
Graph Regression
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- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 239
The regression task is similar to graph classification but using different loss function and performance metric.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Improving the Effective Receptive Field of Message-Passing Neural Networks
A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
Power Spectrum Signatures of Graphs
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Learning Efficient Positional Encodings with Graph Neural Networks
Beyond Message Passing: Neural Graph Pattern Machine
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
Node Regression on Latent Position Random Graphs via Local Averaging
FIT-GNN: Faster Inference Time for GNNs Using Coarsening
GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
HyperAggregation: Aggregating over Graph Edges with Hypernetworks
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
Next Level Message-Passing with Hierarchical Support Graphs
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Learning Long Range Dependencies on Graphs via Random Walks
Spatio-Spectral Graph Neural Networks
A Canonicalization Perspective on Invariant and Equivariant Learning
Graph neural networks with configuration cross-attention for tensor compilers
Conditional Local Feature Encoding for Graph Neural Networks
CKGConv: General Graph Convolution with Continuous Kernels
Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data
An end-to-end attention-based approach for learning on graphs
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Topology-Informed Graph Transformer
Graph Transformers without Positional Encodings
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
Graph Convolutions Enrich the Self-Attention in Transformers!
Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.