Papers Inductive Link Prediction
“Inductive Link Prediction” 태그가 달린 논문 40편 · 필터 해제
Neural Message Passing on Structural Interaction Graphs for Fully-Inductive Graph Neural Networks
A central obstacle in building graph foundation models is the input heterogeneity in terms of feature space dimensionality, semantics, and structure. Such heterogeneity limits the capability of graph neural networks to g…
Inductive Link PredictionModel Graph Inductive Learning for Knowledge Graph Completion
Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations. However, most existing methods derive these embeddings by aggregating only the local neighborhood…
Knowledge Graph CompletionInductive Link PredictionKnowledge GraphsTHOR: Inductive Link Prediction over Hyper-Relational Knowledge Graphs
Knowledge graphs (KGs) have become a key ingredient supporting a variety of applications. Beyond the traditional triplet representation of facts where a relation connects two entities, modern KGs observe an increasing nu…
Inductive Link PredictionKnowledge GraphsGeometric Structural Knowledge Graph Foundation Model
Structural knowledge graph foundation models aim to generalize reasoning to completely new graphs with unseen entities and relations. A key limitation of existing approaches like Ultra is their reliance on a single relat…
Inductive Link PredictionKnowledge GraphsWBT-BGRL: A Non-Contrastive Weighted Bipartite Link Prediction Model for Inductive Learning
Link prediction in bipartite graphs is crucial for applications like recommendation systems and failure detection, yet it is less studied than in monopartite graphs. Contrastive methods struggle with inefficient and bias…
Inductive Link PredictionRecommendation SystemsContrastive LearningType-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models
Inductive link prediction is emerging as a key paradigm for real-world knowledge graphs (KGs), where new entities frequently appear and models must generalize to them without retraining. Predicting links in a KG faces th…
Inductive Link PredictionKnowledge GraphsTowards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling
Temporal Knowledge Graph (TKG) completion models traditionally assume access to the entire graph during training. This overlooks challenges stemming from the evolving nature of TKGs, such as: (i) the model's requirement …
Inductive Link PredictionKnowledge GraphsContext Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Recent investigations on the effectiveness of Graph Neural Network (GNN)-based models for link prediction in Knowledge Graphs (KGs) show that vanilla aggregation does not significantly impact the model performance. In th…
Inductive Link PredictionGraph Neural NetworkKnowledge GraphsInductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoning
N-ary relational facts represent semantic correlations among more than two entities. While recent studies have developed link prediction (LP) methods to infer missing relations for knowledge graphs (KGs) containing n-ary…
Inductive Link PredictionKnowledge GraphsLink PredictionLeap: Inductive Link Prediction via Learnable TopologyAugmentation
Link prediction is a crucial task in many downstream applications of graph machine learning. To this end, Graph Neural Network (GNN) is a widely used technique for link prediction, mainly in transductive settings, where …
Graph Neural NetworkInductive BiasInductive Link PredictionLink Prediction+1Reevaluation of Inductive Link Prediction
Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of randomly sampled negative entities. Due to …
Inductive Link PredictionLink PredictionPredictionFast-and-Frugal Text-Graph Transformers are Effective Link Predictors
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-attributed knowledge graphs. We demonstrate…
Inductive LearningInductive Link PredictionKnowledge GraphsLink PredictionMOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction
Drug-target interaction (DTI) prediction is crucial for identifying new therapeutics and detecting mechanisms of action. While structure-based methods accurately model physical interactions between a drug and its protein…
Drug DiscoveryInductive Link PredictionLink PredictionMulti-Label Zero-Shot Product Attribute-Value Extraction
E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To p…
AttributeAttribute Value ExtractionInductive Link PredictionLink PredictionLink Prediction with Relational Hypergraphs
Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications. Nonetheless, it remains challen…
Graph Neural NetworkInductive Link PredictionKnowledge GraphsLink Prediction+1Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
Link prediction is a crucial research area in knowledge graphs, with many downstream applications. In many real-world scenarios, inductive link prediction is required, where predictions have to be made among unseen entit…
Entity EmbeddingsInductive Link PredictionKnowledge GraphsLink Prediction+1Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing …
Graph Neural NetworkInductive Link PredictionLink PredictionPrediction+1A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the mod…
Inductive Link PredictionKnowledge GraphsLink PredictionEvent Prediction using Case-Based Reasoning over Knowledge Graphs
Applying link prediction (LP) methods over knowledge graphs (KG) for tasks such as causal event prediction presents an exciting opportunity. However, typical LP models are ill-suited for this task as they are incapable o…
Inductive Link PredictionKnowledge GraphsLink PredictionPredictionLearning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction
Inductive link prediction -- where entities during training and inference stages can be different -- has shown great potential for completing evolving knowledge graphs in an entity-independent manner. Many popular method…
Inductive Link PredictionKnowledge GraphsLink Prediction