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This paper introduces GRaCE, an unsupervised framework for generating interpretable graph embeddings using rank-based measures, which outperforms existing methods in retrieval, classification, and clustering tasks on textual and image data.
This paper proposes the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model, which integrates intuitionistic fuzzy sets, graph embedding, and multiview learning to improve classification accuracy and robustness to outliers. Experiments on benchmark datasets show that IFGRVFL-MV outperforms existing models.
This paper introduces and empirically evaluates methods for measuring semantic similarity between knowledge graphs using KG embeddings, proposing EmbPairSim and AvgEmbSim scoring functions that outperform baselines like Sentence-BERT on WikiText-2 and CC-News datasets.
Introduces Adaptive-masking for Graph Embedding (AGE), a Transformer-based self-supervised learning method that addresses latent feature misalignment between graph and text representations for LLMs in GraphRAG tasks by focusing on predicting non-key nodes.
This paper introduces CORE, a new knowledge graph completion model that uses cyclic orthotope relation embeddings on a torus manifold to address boundary constraints in region-based models. Experiments show competitive performance in link prediction tasks.