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Graph Regression

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하위 태스크: 0
평가표: 22
보존된 평가 기록: 239

The regression task is similar to graph classification but using different loss function and performance metric.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/liugangcode/SGIR

공식 코드

Semi-Supervised Graph Imbalanced Regression

https://github.com/lsj2408/Transformer-M

공식 코드

One Transformer Can Understand Both 2D & 3D Molecular Data

https://github.com/lukecavabarrett/pna

공식 코드

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/luoyk1999/hdse

공식 코드

Enhancing Graph Transformers with Hierarchical Distance Structural Encoding

https://github.com/mayabechlerspeicher/tree-g

공식 코드

TREE-G: Decision Trees Contesting Graph Neural Networks

https://github.com/moghadas76/gintrip

공식 코드

GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method

https://github.com/networkslab/ckgconv

공식 코드

CKGConv: General Graph Convolution with Continuous Kernels

https://github.com/nur-ag/elene

공식 코드

Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings

https://github.com/phython96/ARGNP

공식 코드

Automatic Relation-aware Graph Network Proliferation

https://github.com/pku-ml/laplaciancanonization

공식 코드

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

https://github.com/qslim/gnn-spectrum

공식 코드

A New Perspective on the Effects of Spectrum in Graph Neural Networks

https://github.com/qslim/pdf

공식 코드

Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

https://github.com/rampasek/GraphGPS

공식 코드

Recipe for a General, Powerful, Scalable Graph Transformer

https://github.com/scylj1/gnn_edge_regression

공식 코드

Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations

https://github.com/shamim-hussain/egt

공식 코드

Global Self-Attention as a Replacement for Graph Convolution

https://github.com/shamim-hussain/egt_pytorch

공식 코드

Global Self-Attention as a Replacement for Graph Convolution

https://github.com/shamim-hussain/egt_triangular

공식 코드

Global Self-Attention as a Replacement for Graph Convolution

https://github.com/shamim-hussain/ssa

공식 코드

The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles

https://github.com/shamim-hussain/tgt

공식 코드

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/sigeisler/s2gnn

공식 코드

Spatio-Spectral Graph Neural Networks

https://github.com/snap-stanford/ogb

공식 코드

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

https://github.com/tech-srl/how_attentive_are_gats

공식 코드

How Attentive are Graph Attention Networks?

https://github.com/thanhhau097/google_fast_or_slow

공식 코드

Graph neural networks with configuration cross-attention for tensor compilers

https://github.com/tiiuae/gnn-jamming-source-localization

공식 코드

Graph Neural Networks for Jamming Source Localization

https://github.com/tkipf/pygcn

공식 코드

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/toenshoff/CRaWl

공식 코드

Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing

https://github.com/toenshoff/lrgb

공식 코드

Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

https://github.com/twitter-research/cwn

공식 코드

Weisfeiler and Lehman Go Cellular: CW Networks

https://github.com/vijaydwivedi75/gnn-lspe

공식 코드

Graph Neural Networks with Learnable Structural and Positional Representations

https://github.com/vijaydwivedi75/lrgb

공식 코드

Long Range Graph Benchmark

https://github.com/weihua916/powerful-gnns

공식 코드

How Powerful are Graph Neural Networks?

https://github.com/williamleif/GraphSAGE

공식 코드

Inductive Representation Learning on Large Graphs

https://github.com/yCobanoglu/infinite-width-gnns

공식 코드

Infinite Width Graph Neural Networks for Node Regression/ Classification

https://github.com/yuehaowang/sogcn

공식 코드

SoGCN: Second-Order Graph Convolutional Networks

https://github.com/zehong-wang/gpm

공식 코드

Beyond Message Passing: Neural Graph Pattern Machine

https://gitlab.com/drti/swwl

공식 코드

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.