TL;DR
This work presents an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes.
This paper introduces a novel prototype-enhanced hypergraph learning approach for node classification in HINs using hypergraphs instead of graphs, which captures higher-order relationships among nodes and extracts semantic information without relying on metapaths.
This work proposes NumDAE—a novel differential-aware decouple-then-synergize embedding framework for HNKGs that reconstruct numeric semantics based on the relational context to endow numeric entities with algebraic computability, effectively decoupling pure physical quantity from the entangled topological background.
This paper introduces LaGR, a novel approach for integrating global information in knowledge graph reasoning that compresses the graph into a fixed, compact set of latent summaries and applies exact self-attention within this latent space, resulting in scale-invariant attention and stable performance across diverse dat...
A novel self-supervised framework for relational reasoning over RDBs that treats context sparsity as a controllable curriculum variable and leverages it to induce a progressive shift from semantic-dominant inference to structure-aware relational reasoning.
DLP is more effective than state-of-the-art baselines on inductive link prediction and an integration of an LSTM and Transformer architecture is presented to improve the node representations and acquire the order of dependencies and the whole context.
This empirical study shows that candidate rankings change with the selected input, while predicted probabilities remain informative after the class stops changing, and leads to GraphSelect, which starts from individual candidate gains and refines the subset through jointly evaluated exchanges.
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