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/nicola-decao/s-vae-pytorch
공식 코드https://github.com/nicola-decao/s-vae-tf
공식 코드https://github.com/nicolas-hbt/benchmark-sematk
공식 코드Sem@$K$: Is my knowledge graph embedding model semantic-aware?
https://github.com/nicolas-hbt/semantic-lossfunc
공식 코드https://github.com/nicolas-hbt/similar-embeddings
공식 코드https://github.com/nicolas-hbt/versatile-embeddings
공식 코드Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
https://github.com/nikhilnayak-debug/am220
공식 코드Graph Attention for Heterogeneous Graphs with Positional Encoding
https://github.com/ninecl/dekg-ilp
공식 코드Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
https://github.com/ninggirsu/run-gnn
공식 코드Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
https://github.com/niraj17singh/linklogic
공식 코드LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
https://github.com/niuchh/smp
공식 코드Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
https://github.com/nju-websoft/TransEdge
공식 코드TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
https://github.com/nju-websoft/mukg
공식 코드$μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications
https://github.com/nle-ml/mmkb
공식 코드Learning Sequence Encoders for Temporal Knowledge Graph Completion
https://github.com/nomisto/inductiveeval
공식 코드https://github.com/nslab-cuk/connector
공식 코드Connector 0.5: A unified framework for graph representation learning
https://github.com/nslab-cuk/literalkg
공식 코드https://github.com/nslab-cuk/unified-graph-transformer
공식 코드https://github.com/nsssjss/mhgcn
공식 코드https://github.com/ntkien1904/dmns
공식 코드Diffusion-based Negative Sampling on Graphs for Link Prediction
https://github.com/oceanlvr/protoau
공식 코드Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation
https://github.com/oliver-lloyd/kge_param_sens
공식 코드Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality
https://github.com/oom-debugger/ijcai23
공식 코드A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
https://github.com/openmatch/web-dro
공식 코드Enhancing Dense Retrievers' Robustness with Group-level Reweighting
https://github.com/osu-nlp-group/cosmo
공식 코드https://github.com/otamio/kga
공식 코드https://github.com/palash1992/DynamicGEM
공식 코드dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
https://github.com/palash1992/GEM-Benchmark
공식 코드https://github.com/papercodeeric/lfh
공식 코드Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
https://github.com/paramita08/wikidata_auditlp
공식 코드Social Biases in Knowledge Representations of Wikidata separates Global North from Global South
https://github.com/pasalab/sagnas
공식 코드SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
https://github.com/pat-jj/KG-FIT
공식 코드KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
https://github.com/pchlenski/manify
공식 코드https://github.com/pengyanhui/LineaRE
공식 코드LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction
https://github.com/persdre/ex-graph
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.