https://github.com/fuguoji/Event2vec
공식 코드Representation Learning for Heterogeneous Information Networks via Embedding Events
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/fuguoji/GSDN
공식 코드Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
https://github.com/fulifeng/CGI
공식 코드Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
https://github.com/fulifeng/GraphAT
공식 코드Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
https://github.com/galogm/hole
공식 코드https://github.com/gayanku/FDGATII
공식 코드FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping
https://github.com/gdasoulas/lipschitznorm
공식 코드Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
https://github.com/georgeberry/role-action-embeddings
공식 코드Role action embeddings: scalable representation of network positions
https://github.com/geox-lab/cat
공식 코드CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
https://github.com/giannisnik/k-hop-gnns
공식 코드https://github.com/gicheonkang/sglkt-visdial
공식 코드Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
https://github.com/gim4855744/efi-gnn
공식 코드https://github.com/gitgiter/Graph-Adversarial-Learning
공식 코드https://github.com/google-research/google-research
공식 코드Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
https://github.com/google-research/google-research/tree/master/private_personalized_pagerank
공식 코드Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
https://github.com/google-research/graphworld
공식 코드Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
https://github.com/googlebaba/graphnotears
공식 코드Directed Acyclic Graph Structure Learning from Dynamic Graphs
https://github.com/goose315/graph_pooling_benchmark
공식 코드A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
https://github.com/gpfn-anonymous/gpfn
공식 코드Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
https://github.com/gqq1210/psp
공식 코드PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
https://github.com/grand-lab/sublime
공식 코드https://github.com/graph-com/bayesian_inference_based_gnn
공식 코드Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
https://github.com/graph-com/ed-hnn
공식 코드https://github.com/graph-com/nlb
공식 코드Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
https://github.com/graph-com/pair-align
공식 코드https://github.com/graph-lr/graph-aware-logistic-regression
공식 코드Graph as a feature: improving node classification with non-neural graph-aware logistic regression
https://github.com/graphdeeplearning/benchmarking-gnns
공식 코드https://github.com/graphdeeplearning/graphtransformer
공식 코드https://github.com/graphdetec/mgtab
공식 코드MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
https://github.com/graphdetec/rf-gnn
공식 코드RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection
https://github.com/graphdml-uiuc-jlu/geom-gcn
공식 코드https://github.com/graphframex/graphframex
공식 코드GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
https://github.com/grvndnz/parallel-fnn
공식 코드https://github.com/gsl-benchmark/gslb
공식 코드https://github.com/guoji-fu/pgnns
공식 코드https://github.com/gxm1141/ma-gcl
공식 코드MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.