https://github.com/aliaaz99/grassrep
공식 코드GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly
Papers with Code 아카이브
Node Classification
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Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.
Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.
<span style="color:grey; opacity: 0.6">( Image credit: Fast Graph Representation Learning With PyTorch Geometric )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alibaba/graphtranslator
공식 코드GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
https://github.com/alipay/rgsl
공식 코드https://github.com/allemanenti/Learning-Calibrated-Structures
공식 코드https://github.com/amaiya/ktrain
공식 코드https://github.com/amazon-research/gcp-with-gnns-example
공식 코드https://github.com/amazon-research/gnn-tail-generalization
공식 코드Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
https://github.com/amazon-research/tgl
공식 코드TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
https://github.com/amazon-science/convolution-matching
공식 코드Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
https://github.com/amazon-science/network-usable-info-framework
공식 코드NetInfoF Framework: Measuring and Exploiting Network Usable Information
https://github.com/amblee0306/label-non-uniformity-gnn
공식 코드Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
https://github.com/amoliu/CayleyNet
공식 코드CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
https://github.com/anacletolab/grape
공식 코드GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
https://github.com/andompesta/nodeembedding-to-communityembedding
공식 코드https://github.com/andyjzhao/wsdm23-gsr
공식 코드Self-Supervised Graph Structure Refinement for Graph Neural Networks
https://github.com/anonymous1252022/kr_for_gnns
공식 코드https://github.com/anonymous1252022/recoverability
공식 코드https://github.com/anoushkavyas/dglr
공식 코드https://github.com/antoniolonga/explaining-the-explainers-in-graph-neural-networks
공식 코드Explaining the Explainers in Graph Neural Networks: a Comparative Study
https://github.com/anwar-said/esgea
공식 코드Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
https://github.com/applied-machine-learning-lab/mesh-tkgr
공식 코드https://github.com/arash-dehghan/EmbeddingComplexNetworks
공식 코드https://github.com/arashrasti96/gstam
공식 코드GSTAM: Efficient Graph Distillation with Structural Attention-Matching
https://github.com/aravindsankar28/DySAT
공식 코드Dynamic Graph Representation Learning via Self-Attention Networks
https://github.com/aravindsankar28/Meta-GNN
공식 코드https://github.com/arichibald/grafourierformer
공식 코드Graph Fourier Transformer with Structure-Frequency Information
https://github.com/arjunsubramonian/degree-bias-exploration
공식 코드Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
https://github.com/arpitdm/gnn_accuracy_fairness_tradeoff
공식 코드Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification
https://github.com/artessay/ragraph
공식 코드RAGraph: A General Retrieval-Augmented Graph Learning Framework
https://github.com/arvin0313/mecoin-gfscil
공식 코드An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
https://github.com/arvindh75/cafin
공식 코드https://github.com/ashgeek/lpgnet-prototype
공식 코드https://github.com/automl-research/autograph-kddcup2020
공식 코드https://github.com/avmoldovan/Heterophily_and_oversmoothing-forked
공식 코드https://github.com/balcilar/Spectral-Designed-Graph-Convolutions
공식 코드Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
https://github.com/banghonor/disco
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.