arXiv Machine Learning June 24, 2026 By Aleksandar Stankovi\'c, Haoran Du, Xinming Wang

FuseSampleAgg: One-Pass Neighborhood Estimation for Budgeted Knowledge-Graph Refresh and Validation

Read the original on arXiv Machine Learning →

arXiv:2511. 13645v2 Announce Type: replace Abstract: Operational knowledge-graph (KG) pipelines in networking and cybersecurity increasingly need to refresh embeddings under strict time, memory, and audit budgets, especially as curated feeds and LLM-assisted extraction accelerate KG updates.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism

Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs

SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs