AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
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.
Deep sequential encoders such as Transformers are widely used for modeling user behavior in sequential recommendation, yet their effectiveness can be limited when interaction sequences are short and sparse. In such settings, insufficient behavioral evidence makes it difficult to learn reliable representations. Existing contrastive learning methods provide useful auxiliary supervision, but they often rely on random sequence perturbations that may remove informative interactions and typically apply contrastive objectives only to the final encoder layer. Moreover, interaction-based models cannot fully capture global semantic relations among items that rarely or never co-occur. To address these limitations, we propose Semantic Global Graph with Multi-layer Alignment for Recommendation (SeGMA4Rec), which combines item-level semantic enrichment with sequence-level hierarchical regularization. SeGMA4Rec constructs a semantic item graph from pretrained textual embeddings, adaptively refines the retained edge weights, and propagates task-relevant information among related items. The graph-enhanced item representations are then processed by a sequential encoder whose attention scores guide probabilistic view generation toward retaining informative interactions. Three contrastive objectives further align representations across augmented views and encoder layers, providing direct regularization at multiple depths. Experiments on four benchmark datasets show that SeGMA4Rec achieves strong and generally superior performance over competitive baselines, including improvements of up to 9.9% in Recall@10 and 8.1% in NDCG@10 on ML-1M. Additional analyses confirm the complementary contributions of semantic graph propagation, adaptive edge refinement, attention-guided augmentation, and multi-layer contrastive alignment.
Knowledge Graph Completion (KGC) aims to predict missing links in knowledge bases. However, existing methods often struggle with reasoning over complex semantic relations and generate predictions that violate common-sense logic. In this paper, a knowledge graph completion model based on semantic constrained hybrid scoring (SCHS) is proposed, which integrates three synergistic mechanisms. First, an Adaptive Feature Gating (AFG) mechanism is designed for feature recalibration. Unlike static embeddings, this module dynamically recalibrates the importance of different semantic aspects based on the relation context. Second, a Relation-Concept Matrix Projection (RCMP) mechanism is introduced to act as an explicit semantic constraint. This mechanism maps triplets to a latent concept space to capture the inherent consistency between relations and concepts, providing a penalization mechanism to filter out noise that is topologically close but semantically invalid. Third, an Adaptive Fusion Strategy (AFS) utilizing a soft-start mechanism is introduced to effectively balance structural and semantic scores without early-training collapses. This tri-fold synergy essentially resolves the rigid trade-off between geometric alignment and flexible semantic matching. Extensive experiments on the WN18RR and FB15k-237 datasets demonstrate that SCHS achieves substantial performance improvements, verifying the robustness and effectiveness of the dynamically regulated fusion of semantics and structures in the alleviation of long-tail errors.
An adaptive geometric hypergraph encoder that projects entities into a product manifold and design a topology-aware gating mechanism to dynamically select optimal geometric spaces for each entity, and a role-aware Transformer equipped with role-specific projections and micro-structural bias injection to refine embeddings.
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.
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