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Graph Mining

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/yushundong/graph-mining-fairness-data

공식 코드

Fairness in Graph Mining: A Survey

https://github.com/yushundong/pygdebias

공식 코드

Fairness in Graph Mining: A Survey

https://github.com/zfjsail/oag-bench

공식 코드

OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining

https://github.com/zfx233/CMGNN

공식 코드

Revisiting the Message Passing in Heterophilous Graph Neural Networks

https://github.com/zjunet/G-Tuning

공식 코드

Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns

https://github.com/zjunet/graphskeleton

공식 코드

Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph

https://github.com/zjunlp/relphormer

공식 코드

Relphormer: Relational Graph Transformer for Knowledge Graph Representations

https://github.com/zkhku/fedsage

공식 코드

Subgraph Federated Learning with Missing Neighbor Generation

https://github.com/zwt233/DGMLP

공식 코드

Evaluating Deep Graph Neural Networks

https://gitlab.com/lianeg/sports-entailment-evaluation

공식 코드

Blindness to Modality Helps Entailment Graph Mining

https://gitlab.com/lianeg/temporal-entailment-sports-dataset

공식 코드

Incorporating Temporal Information in Entailment Graph Mining

https://github.com/RingBDStack/DGGAN

Adversarial Directed Graph Embedding

https://github.com/VVRoseth/2.-Identifying_misinformation_in_disasters_FEMA

False Information on Web and Social Media: A Survey

https://github.com/benedekrozemberczki/benedekrozemberczki

Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

https://github.com/bwoodhamilton/Social-Media-Misinformation-During-Disasters

False Information on Web and Social Media: A Survey

https://github.com/bwoodhamilton/client_project_group_3

False Information on Web and Social Media: A Survey

https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/safe-graph/dgfraud-tf2

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/shchur/gnn-benchmark

Pitfalls of Graph Neural Network Evaluation

https://github.com/thudm/graphcad

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/toinesayan/node-classification-and-label-dependencies

Pitfalls of Graph Neural Network Evaluation

https://github.com/yimiachack/gskn

Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

https://github.com/zhliping/Deep-Learning

Attention Models in Graphs: A Survey

https://github.com/zhuyulin1994/BinarizedAttack

BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection

https://github.com/zjunet/amnet

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

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