https://github.com/hysonlab/biomedkg
공식 코드Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
Papers with Code 아카이브
Link Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 79
- 보존된 평가 기록
- 497
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/hysonlab/drug-interactions
공식 코드Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
https://github.com/hytruongson/mgvae
공식 코드https://github.com/hyunjinhwn/sigir22-ahp
공식 코드https://github.com/ibalazevic/HypER
공식 코드https://github.com/ibalazevic/TuckER
공식 코드https://github.com/ibalazevic/multirelational-poincare
공식 코드https://github.com/ibpa/kglm
공식 코드KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction
https://github.com/idea-iitd/GraphReach
공식 코드GraphReach: Position-Aware Graph Neural Network using Reachability Estimations
https://github.com/idirlab/kgcompletion
공식 코드Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study
https://github.com/ifm-mag/HyDRO_PLUS
공식 코드Efficient and Privacy-Preserved Link Prediction via Condensed Graphs
https://github.com/iieir-km/RUGE
공식 코드Knowledge Graph Embedding with Iterative Guidance from Soft Rules
https://github.com/illdepence/contextgraph
공식 코드CoCon: A Data Set on Combined Contextualized Research Artifact Use
https://github.com/ilyatyagin/dyport
공식 코드Dyport: Dynamic Importance-based Hypothesis Generation Benchmarking Technique
https://github.com/im0qianqian/WSDM2022TGP-AntGraph
공식 코드https://github.com/inpefess/neural-semigroups
공식 코드https://github.com/intfloat/simkgc
공식 코드SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models
https://github.com/ireklos/pathe
공식 코드PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
https://github.com/irenezihuili/cgprompt
공식 코드https://github.com/irenezihuili/nnkgc
공식 코드NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods
https://github.com/ivam-he/DirLinkBench-SDGAE
공식 코드https://github.com/iyempissy/rebMIGraph
공식 코드https://github.com/jayachaturvedi/pain_in_mental_health
공식 코드https://github.com/jayroxis/Link-Injection
공식 코드https://github.com/jd445/TrainAnuualBERT
공식 코드Towards understanding evolution of science through language model series
https://github.com/jean-kouagou/enhancedrulelearning
공식 코드https://github.com/jeffrey-sardina/twig-release-1.0
공식 코드https://github.com/jhl-hust/titer
공식 코드TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
https://github.com/jhljx/CTGCN
공식 코드K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
https://github.com/jhood3/Hypergraphs
공식 코드Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networks
https://github.com/jiaruzouu/prb
공식 코드https://github.com/jisungyoon/persona2vec
공식 코드Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
https://github.com/jiyaowei/lp_nkgs
공식 코드https://github.com/js05212/PyTorch-for-NPN
공식 코드Natural-Parameter Networks: A Class of Probabilistic Neural Networks
https://github.com/juanhui28/heart
공식 코드Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
https://github.com/juellsprott/graphair-reproducibility
공식 코드Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.