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Link Prediction

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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/dice-group/Convolutional-Hypercomplex-Embeddings-for-Link-Prediction

공식 코드

Convolutional Hypercomplex Embeddings for Link Prediction

https://github.com/dice-group/OOV-In-Link-Prediction

공식 코드

Out-of-Vocabulary Entities in Link Prediction

https://github.com/dice-group/dice-embeddings

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Kronecker Decomposition for Knowledge Graph Embeddings

https://github.com/dice-group/universal_embeddings

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Universal Knowledge Graph Embeddings

https://github.com/diligmic/rcbm-neurips2024

공식 코드

Relational Concept Bottleneck Models

https://github.com/diningphil/e-cgmm

공식 코드

Modeling Edge Features with Deep Bayesian Graph Networks

https://github.com/divelab/sopool

공식 코드

Second-Order Pooling for Graph Neural Networks

https://github.com/dki-lab/joint-kb-text-embedding

공식 코드

A Systematic Investigation of KB-Text Embedding Alignment at Scale

https://github.com/dmzou/SCAT

공식 코드

Encoding Robust Representation for Graph Generation

https://github.com/dongkikim95/d-sla

공식 코드

Graph Self-supervised Learning with Accurate Discrepancy Learning

https://github.com/doujiang-zheng/tip-gnn

공식 코드

Transition Propagation Graph Neural Networks for Temporal Networks

https://github.com/dream-lm/ingb_framework

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INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification

https://github.com/ds-jrg/causal-qa-rl

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Causal Question Answering with Reinforcement Learning

https://github.com/duyguislakoglu/temt

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Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs

https://github.com/eXascaleInfolab/daor

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Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection

https://github.com/eaglew/clbd

공식 코드

SciMON: Scientific Inspiration Machines Optimized for Novelty

https://github.com/ecml-peagnn/PEAGNN

공식 코드

Metapath- and Entity-aware Graph Neural Network for Recommendation

https://github.com/edisonleeeee/graph-adversarial-learning

공식 코드

A Survey of Adversarial Learning on Graphs

https://github.com/edisonleeeee/maskgae

공식 코드

What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders

https://github.com/ehsankazemi/adaptiveSubseq

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Adaptive Sequence Submodularity

https://github.com/ejwww/tangnn

공식 코드

TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning

https://github.com/elsevier-ai-lab/bioblp

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BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs

https://github.com/erfanloghmani/effect-of-loss-function-tbatching

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Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks

https://github.com/erfanloghmani/myket-android-application-market-dataset

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Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks

https://github.com/erfmah/answering_graph_queries

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Deep Generative Models for Subgraph Prediction

https://github.com/ethanhezhao/NARM

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Leveraging Node Attributes for Incomplete Relational Data

https://github.com/exiled1143/retrieval-augmented-language-model-for-multi-label-knowledge-graph-link-prediction

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Retrieval-Augmented Language Model for Extreme Multi-Label Knowledge Graph Link Prediction

https://github.com/facebookresearch/SEAL_OGB

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Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning

https://github.com/facebookresearch/glemos

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GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection

https://github.com/facebookresearch/kbc

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Canonical Tensor Decomposition for Knowledge Base Completion

https://github.com/facebookresearch/shaDow_GNN

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Decoupling the Depth and Scope of Graph Neural Networks

https://github.com/facebookresearch/ssl-relation-prediction

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Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

https://github.com/facebookresearch/tkbc

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Tensor Decompositions for temporal knowledge base completion

https://github.com/faisal-iut/linkPrediction

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Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction

https://github.com/flyingdoog/rwm

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Random Walk on Multiple Networks

https://github.com/fpour/dgb

공식 코드

Towards Better Evaluation for Dynamic Link Prediction

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