Tag
This paper proposes an adaptive spectral bandwidth control method for kernelized graph construction to align kernel spectral properties with intrinsic manifold dimensions, showing improvements in self-supervised learning embedding tasks on CIFAR-100.
This paper proposes a bidirectional Mamba model for long-term behavioral evaluation to enhance trustworthy collaborator selection in distributed systems, demonstrating improved accuracy over baseline methods.
SNAP-KG is a framework that enables efficient integration of streaming entities into knowledge graphs via multi-view clustering and inductive inference, reducing inference time and candidate search space for downstream tasks like entity resolution and link prediction.
This paper introduces AutoGrable, a method that scores candidate graph constructions for tabular data without training a GNN, using a label-alignment risk based on 1-WL color refinement, enabling cheap search for effective table-to-graph mappings.
ContextRAG introduces an extraction-free method for constructing hierarchical graph indices for retrieval-augmented generation, using Residual-Quantization K-Means and Formal Concept Analysis to reduce LLM calls and tokens by orders of magnitude while maintaining competitive F1 scores on multi-hop questions.