Papers with Code 아카이브
Link Prediction
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- 평가표
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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/taokz/FedR
공식 코드https://github.com/tech-srl/how_attentive_are_gats
공식 코드https://github.com/tensorflow/neural-structured-learning
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/tewiSong/Rot-Pro
공식 코드Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
https://github.com/tfahim15/had2024
공식 코드https://github.com/thiviyanT/torch-rgcn
공식 코드https://github.com/thunlp/AGE
공식 코드https://github.com/timelovercc/awesome-graph-causal-learning
공식 코드https://github.com/timpostuvan/CTDG-link-anomaly-detection
공식 코드Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
https://github.com/timpostuvan/adagrid
공식 코드AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
https://github.com/timpostuvan/tgb-link-anomaly-detection-baselines
공식 코드Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
https://github.com/tkipf/gae
공식 코드https://github.com/tkipf/relational-gcn
공식 코드https://github.com/tmlr-group/one-shot-subgraph
공식 코드Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
https://github.com/tmlr-group/rgib
공식 코드https://github.com/toenshoff/lrgb
공식 코드Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
https://github.com/toneli/hnzslp
공식 코드A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
https://github.com/toneli/petailor-for-bio-triple-extraction
공식 코드BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine
https://github.com/torchkge-team/torchkge
공식 코드https://github.com/tranhungnghiep/AnalyzeKGE
공식 코드Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
https://github.com/tranhungnghiep/KG20C
공식 코드Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
https://github.com/tranhungnghiep/MEI-KGE
공식 코드Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
https://github.com/tranhungnghiep/meim-kge
공식 코드https://github.com/tsafavi/cascader
공식 코드CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction
https://github.com/tsafavi/codex
공식 코드https://github.com/ttrouill/complex
공식 코드https://github.com/twelfth-star/LinkGPT
공식 코드LinkGPT: Teaching Large Language Models To Predict Missing Links
https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding
공식 코드Adversarial Privacy Preserving Graph Embedding against Inference Attack
https://github.com/uark-cviu/DyGLIP
공식 코드https://github.com/ubc-systopia/CATWalk
공식 코드https://github.com/uclmr/jack
공식 코드https://github.com/uclnlp/ctp
공식 코드Differentiable Reasoning on Large Knowledge Bases and Natural Language
https://github.com/uclnlp/gntp
공식 코드Differentiable Reasoning on Large Knowledge Bases and Natural Language
https://github.com/uclnlp/ntp
공식 코드Differentiable Reasoning on Large Knowledge Bases and Natural Language
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.