Papers with Code 아카이브
Link Prediction
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Link Prediction is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.
<span style="color:grey; opacity: 0.6">( Image credit: Inductive Representation Learning on Large Graphs )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rathapech/DTI_LMP
공식 코드A generalized method toward drug-target interaction prediction via low-rank matrix projection
https://github.com/renli1024/se-gnn
공식 코드How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
https://github.com/riannevdberg/gc-mc
공식 코드https://github.com/ringbdstack/dgib
공식 코드https://github.com/ringbdstack/sgdd
공식 코드Does Graph Distillation See Like Vision Dataset Counterpart?
https://github.com/rinnesz/clga
공식 코드Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
https://github.com/rmanluo/gsnop
공식 코드Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
https://github.com/rocmfang/distger
공식 코드Distributed Graph Embedding with Information-Oriented Random Walks
https://github.com/roshnigiyer/br-gcn
공식 코드https://github.com/rui9812/vlp
공식 코드To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
https://github.com/ryienh/dggen
공식 코드A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
https://github.com/samihaija/tf-fsvd
공식 코드https://github.com/sanaz11-3/temporal-link-prediction
공식 코드https://github.com/sang-woo-seo/TGIB
공식 코드Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck
https://github.com/saynbabul/weak-walk
공식 코드https://github.com/sbonner0/temporal-offset-reconstruction
공식 코드Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
https://github.com/scottjiao/slotgat_icml23
공식 코드SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
https://github.com/scylj1/gnn_edge_regression
공식 코드Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
https://github.com/sczwangxiao/radar-mm2022
공식 코드Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation
https://github.com/seaocn/link-backdoor
공식 코드Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection
https://github.com/seongjunyun/Graph_Transformer_Networks
공식 코드https://github.com/seongjunyun/neo-gnns
공식 코드https://github.com/seongjunyun/neo_gnns
공식 코드Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
https://github.com/seukgcode/fastkge
공식 코드Fast and Continual Knowledge Graph Embedding via Incremental LoRA
https://github.com/shamim-hussain/egt
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_pytorch
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_triangular
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/tgt
공식 코드https://github.com/shantanu95/exposure-bias-link-rec
공식 코드https://github.com/shawnkx/Decom
공식 코드Decompressing Knowledge Graph Representations for Link Prediction
https://github.com/sherylhyx/msgnn
공식 코드MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
https://github.com/shilinsun/lgcl
공식 코드https://github.com/shizhouxing/DialogueDiscourseParsing
공식 코드A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues
https://github.com/shl5133/E2EECPE
공식 코드End-to-end Emotion-Cause Pair Extraction via Learning to Link
https://github.com/shoron-dutta/cblip
공식 코드Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.