https://github.com/hanchen-hust/csgcl
공식 코드CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
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/hanpx20/groupdro_dense_retrieval
공식 코드Enhancing Dense Retrievers' Robustness with Group-level Reweighting
https://github.com/harryshomer/lpformer
공식 코드https://github.com/harveyp123/iccd_sptrn_slr
공식 코드https://github.com/haseebs/OWE
공식 코드An Open-World Extension to Knowledge Graph Completion Models
https://github.com/hessamla/forcedirected
공식 코드https://github.com/hestiasky/imf-pytorch
공식 코드IMF: Interactive Multimodal Fusion Model for Link Prediction
https://github.com/hexiangnan/neural_factorization_machine
공식 코드Neural Factorization Machines for Sparse Predictive Analytics
https://github.com/hextrato/KER
공식 코드https://github.com/hit-ices/tnnls-mtgn
공식 코드Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
https://github.com/hkuds/awesome-llm4graph-papers
공식 코드https://github.com/hkuds/diffgraph
공식 코드https://github.com/hkuds/opengraph
공식 코드https://github.com/hkust-knowcomp/privategraphencoder
공식 코드Independent Distribution Regularization for Private Graph Embedding
https://github.com/hmthanh/GCAT
공식 코드Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
https://github.com/hongluzhou/dhypr
공식 코드https://github.com/honoluluuuu/graphattacker
공식 코드https://github.com/hope-rita/thlm
공식 코드Pretraining Language Models with Text-Attributed Heterogeneous Graphs
https://github.com/hopkin-ghp/QuatDE
공식 코드QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
https://github.com/houchengbin/ABRW
공식 코드Attributed Network Embedding for Incomplete Attributed Networks
https://github.com/houchengbin/DynWalks
공식 코드DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
https://github.com/houyili/gipa_wide_deep
공식 코드GIPA: A General Information Propagation Algorithm for Graph Learning
https://github.com/hse-cs/migtf
공식 코드Knowledge Graph Completion with Mixed Geometry Tensor Factorization
https://github.com/hse-dyngraph-research-team/dyngraphmodelling
공식 코드Temporal Graph Network Embedding with Causal Anonymous Walks Representations
https://github.com/hsinghuan/novel-node-category-detection
공식 코드https://github.com/huangjunjie95/SiGAT
공식 코드https://github.com/huawei-noah/benchmark
공식 코드TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
https://github.com/huawei-noah/noah-research
공식 코드DyG2Vec: Efficient Representation Learning for Dynamic Graphs
https://github.com/huawei-noah/noah-research/tree/master/graph_atlas
공식 코드DyG2Vec: Efficient Representation Learning for Dynamic Graphs
https://github.com/huda-hakami/learning-to-borrow-for-kgs
공식 코드https://github.com/hwq0726/sdoh-kg
공식 코드https://github.com/hwq0726/sdohenpkg
공식 코드https://github.com/hwwang55/GraphGAN
공식 코드GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/hwwang55/KGCN
공식 코드Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/hxyscotthuang/cmpnn
공식 코드A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
https://github.com/hyleepp/continue-kgc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.