Papers with Code 아카이브
Graph Representation Learning
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The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/akirato/perm-gaussiankg
공식 코드Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
https://github.com/alejgh/wikidata-edithist-refinement
공식 코드Leveraging Wikidata's edit history in knowledge graph refinement tasks
https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE
공식 코드PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
https://github.com/alipay/parameter_inference_efficient_pie
공식 코드https://github.com/anacletolab/grape
공식 코드GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
https://github.com/andyweizhao/graphs-normed-spaces
공식 코드https://github.com/angusmonroe/unionsnn
공식 코드https://github.com/anonymous1252022/kr_for_gnns
공식 코드https://github.com/anonymous1252022/recoverability
공식 코드https://github.com/anrep/house
공식 코드HousE: Knowledge Graph Embedding with Householder Parameterization
https://github.com/ansonb/FeTA_TMLR
공식 코드How Expressive are Transformers in Spectral Domain for Graphs?
https://github.com/ansonb/hopfe
공식 코드HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
https://github.com/aoluming/IDKG
공식 코드https://github.com/apliko-xyz/pytdc
공식 코드https://github.com/apoorvumang/kgt5
공식 코드Sequence-to-Sequence Knowledge Graph Completion and Question Answering
https://github.com/april-tools/gekcs
공식 코드How to Turn Your Knowledge Graph Embeddings into Generative Models
https://github.com/aravindsankar28/DySAT
공식 코드Dynamic Graph Representation Learning via Self-Attention Networks
https://github.com/armitakhn/gilbert
공식 코드https://github.com/arvin0313/mecoin-gfscil
공식 코드An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
https://github.com/arvindh75/cafin
공식 코드https://github.com/aryopg/biokge
공식 코드https://github.com/astolman/snlpy
공식 코드https://github.com/astrazeneca/biomedical-kg-topological-imbalance
공식 코드Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
https://github.com/astrazeneca/kgem-in-drug-discovery
공식 코드Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
https://github.com/bdi-lab/bive
공식 코드Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
https://github.com/bdi-lab/hynt
공식 코드Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
https://github.com/bdi-lab/ingram
공식 코드InGram: Inductive Knowledge Graph Embedding via Relation Graphs
https://github.com/bdi-lab/reed
공식 코드PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
https://github.com/bigdasgit/dab-gnn
공식 코드https://github.com/bioinfomachinelearning/gcpnet
공식 코드Geometry-Complete Perceptron Networks for 3D Molecular Graphs
https://github.com/blingbell/msngo
공식 코드https://github.com/bluer555/KernelGCN
공식 코드Rethinking Kernel Methods for Node Representation Learning on Graphs
https://github.com/borealisai/staypositive
공식 코드Stay Positive: Knowledge Graph Embedding Without Negative Sampling
https://github.com/borgwardtlab/neuralwalker
공식 코드https://github.com/borgwardtlab/sat
공식 코드Structure-Aware Transformer for Graph Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.