Papers Link Prediction
“Link Prediction” 태그가 달린 논문 2,136편 · 필터 해제
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionNeural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding mod…
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionHierarchy-Aware Semantic Losses for Knowledge Graph Link Prediction
Knowledge graphs are often accompanied by ontological class hierarchies that encode valuable semantic information, yet many link prediction methods either ignore such hierarchies or incorporate them indirectly through ad…
Representation LearningGraph Neural NetworkKnowledge GraphsLink PredictionReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning
Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal graph neural networks (GNNs) focus on al…
Graph structure learningRepresentation LearningNode ClassificationLink PredictionFairness-Aware Network Embeddings: Methods, Applications, and Challenges
Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization. However, real-world networks …
Representation LearningGraph Neural NetworkNode ClassificationLink PredictionDynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a single, fixed embedding space. In this …
Node ClassificationLink PredictionNonlinear Laplacians Improve Signed-Directed Graph Learning
While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We int…
Node ClassificationLink PredictionGraph LearningSame Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link prediction (LP). However, existing evalua…
Node ClassificationLink PredictionKGCQual: An Interpretable Framework for Evaluating the Knowledge Graph Construction Quality from Text
Knowledge Graphs (KGs) are increasingly constructed through automated extraction pipelines; however, such systems often introduce spurious or incomplete triples, which degrade downstream performance. Existing evaluation …
Knowledge GraphsLink PredictionTSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics…
Node ClassificationLink PredictionFraud DetectionCanopy: A Heterograph Foundation Model for Metabolic Engineering
Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering. Existing computational approaches either rely on stoichiometric constraint…
Link PredictionTowards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustwort…
Node Property PredictionGraph ClassificationLink PredictionPoisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models. As knowledge graphs evolve over time and …
Knowledge GraphsLink PredictionTAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Existing approaches often handle the two modalities separately: graph …
Node ClassificationLink PredictionGraph LearningBeyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formul…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRelevance Is Not Permission: Warranted Attention for Value Contributions
Relevance is not permission. Attention lets a model read key-value items related to the current query, but it does not guarantee that the value contribution of such an item becomes prediction evidence. A retrieved passag…
Link PredictionRelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modelin…
Knowledge Graph CompletionKnowledge GraphsLink PredictionEstimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty. We study this issue by c…
Link PredictionA Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks
In this study, we propose a novel pipeline for generic, model-agnostic, local-level counterfactual explainability in graph neural networks (GNNs). Although counterfactual explainers capable of both adding and removing ed…
Explanation GenerationGraph ClassificationLink PredictionEarly-Exit Graph Neural Networks for Link Prediction
Graph Neural Networks are great for link prediction in various network-like structures; however, the question of their speed/quality tradeoff has been barely studied. While in practice the time it takes to do inference m…
Link Prediction