#link-prediction News & Analysis

6 articles tagged with #link-prediction. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · 1d ago7/10

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Plain Transformers are Surprisingly Powerful Link Predictors

Researchers introduce PENCIL, a plain Transformer model that outperforms Graph Neural Networks at link prediction by using attention over sampled local subgraphs instead of complex structural encodings. The approach demonstrates that simpler architectural choices can achieve superior performance while maintaining scalability and parameter efficiency, challenging the industry's reliance on elaborate engineering techniques.

AINeutralarXiv – CS AI · May 116/10

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HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

Researchers introduce HYPER, a foundation model for predicting missing connections in knowledge hypergraphs that can generalize to novel entities and relation types unseen during training. The model advances inductive link prediction by encoding entity positions within hyperedges, enabling transfer learning across relations of varying complexity, with evaluation on 16 new datasets showing consistent outperformance of existing methods.

AINeutralarXiv – CS AI · May 96/10

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Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models

Researchers propose using graphlets—small recurring subgraph patterns—as structural tokens for Knowledge Graph Foundation Models (KGFMs), enabling better transfer learning across diverse knowledge graphs. Testing on 51 knowledge graphs demonstrates that this approach outperforms existing KGFMs for zero-shot link prediction tasks.

AIBullisharXiv – CS AI · Apr 156/10

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Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs

Researchers introduce RALP, a novel method that uses chain-of-thought prompts with large language models to improve knowledge graph predictions, outperforming traditional embedding models by over 5% on standard benchmarks while better handling unseen entities, relations, and numerical data.

AIBullisharXiv – CS AI · Mar 45/104

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VL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings

Researchers have developed VL-KGE, a new framework that combines Vision-Language Models with Knowledge Graph Embeddings to better process multimodal knowledge graphs. The approach addresses limitations in existing methods by enabling stronger cross-modal alignment and more unified representations across diverse data types.

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AINeutralarXiv – CS AI · Mar 54/10

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TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction

Researchers propose TFWaveFormer, a novel Transformer architecture that combines temporal-frequency analysis with multi-resolution wavelet decomposition for dynamic link prediction. The framework achieves state-of-the-art performance on benchmark datasets by better capturing complex multi-scale temporal dynamics in applications like social networks and financial modeling.