AdaGeM: Adaptive Geometric Learning on Manifolds for Hyper-Relational Knowledge Graphs

ACKGE: knowledge graph embedding with probabilistic semantic bounds for adaptive contrastive learning

The Adaptive Contrastive Knowledge Graph Embedding (ACKGE) framework, which uses the empirical mean and standard deviation of entity similarities to define probabilistic semantic bounds, achieves competitive performance against strong baselines in link prediction tasks, particularly in handling complex relational structures.

Learning curvature-aware multimodal representations with bicomplex embeddings

GeoKR-Net: Geometry-Aware Representation Learning With Knowledge Anchoring for Sensitive Information Detection

With the increasing complexity of online communication, sensitive content frequently appears in variant, implicit, and context-dependent forms, posing significant challenges to conventional flat detection approaches. Domain-specific fine-tuning often induces representation anisotropy and semantic feature entanglement, leading to distorted semantic manifolds and unstable decision boundaries. Moreover, existing methods rarely consider the inherent ordinal dependencies among risk levels, limiting their reliability for fine-grained sensitivity assessment. To address these challenges, we propose GeoKR-Net, a hierarchically structured framework that jointly integrates geometric semantic refinement, adaptive knowledge anchoring, and hierarchical risk-aware inference. At the representation level, a geometric refinement operator performs isotropic correction and saliency-guided feature purification, reconstructing a balanced and discriminative semantic manifold. At the knowledge level, a dual-track adaptive anchoring mechanism explicitly models heterogeneous semantic evolution patterns through structured perturbation exploration and latent manifold expansion, enabling the transition from lexical matching to conceptual alignment while maintaining robustness under knowledge-cold-start scenarios. At the decision level, a hierarchical consistency constraint explicitly captures ordinal dependencies among risk levels, ensuring logically coherent and stable multi-level risk prediction. Extensive experiments conducted on four sensitive benchmarks and three general-domain datasets demonstrate that GeoKR-Net consistently outperforms strong baseline models. Further analyses of representation geometry and knowledge coverage verify its effectiveness in mitigating semantic anisotropy, enhancing risk-sensitive feature discrimination, and reducing reliance on static lexicons. The results highlight the importance of jointly modeling geometric structure, adaptive knowledge evolution, and hierarchical semantics for robust and interpretable sensitive information detection.

Differential-Aware Synergy of Quantity and Topology for Hyper-Relational Knowledge Graphs with Numeric Entities

Representation learning on hyper-relational numeric knowledge graphs (HNKGs), which incorporate numeric entities and facts consisting of a primary triple augmented by attribute-value qualifiers, is crucial for advanced reasoning. However, most existing methods employ discretization or binning strategies, struggling to consider physical quantities in modeling numeric entities: neglecting numeric continuity (e.g., 100 is greater than 1) and numeric polysemy (e.g., 80 represent age or weight without unit), leading to feature entanglement between numeric semantics and topological structures. In this predicament, standard attention mechanisms—limited by the inherent nature of positive-only accumulation (i.e., non-negative weights)-fail to peel away quantitative noise, forcing models into a compromised balance. To address these challenges, we propose NumDAE—a novel differential-aware decouple-then-synergize embedding framework for HNKGs. We 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. Simultaneously, we design a differential-aware embedding transformer to effectively aggregate the semantic and structural representations, utilizing a subtractive cancellation between signal and noise attention maps to avoid mutual interference arising from the misalignment, achieving a high-fidelity synergy. Extensive experiments on public HNKG benchmarks demonstrate that NumDAE outperforms competitive baselines on link, relation and numeric value prediction tasks.

Unsupervised Graph Representation Learning with Complementary View Alignment

This framework introduces a dual-encoder architecture that separately processes structural and attribute information, incorporates node positional encoding to approximate Neighborhood Identity Distribution (NID), and employs dual reconstruction tasks for both edges and node attributes.

Dual-Topology Learning with Adaptive Anchors for Multi-View Clustering

The Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold.