arXiv Machine Learning July 2, 2026 By Peng Fang, Arijit Khan, Ziqiang Wu, Zhenli Li, Yibo Zhou, Fang Wang, Dan Feng

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

Read the original on arXiv Machine Learning →

arXiv:2606. 22180v2 Announce Type: replace-cross Abstract: Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (GraphRAG).

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

arXiv Machine Learning

Aug 10

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

arXiv:2608. 06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges.

By Guofan Yu, Sitian Chen, Zhenheng Tang, Xiaowen Chu, Amelie Chi Zhou

rag benchmarks safety data-engineering

arXiv Machine Learning

Jun 24

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

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.

By Aleksandar Stankovi\'c, Haoran Du, Xinming Wang

llms rag benchmarks

arXiv AI

Aug 11

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

arXiv:2608. 07733v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems.

By Liad Gerstman, Aditya Dhakal, Dejan Milojicic, Avi Mendelson

efficiency benchmarks

arXiv Machine Learning

Jul 21

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

arXiv:2607. 17374v1 Announce Type: cross Abstract: Graph Neural Network (GNN) inference on billion-scale graphs is challenging due to the large memory footprint of features and embeddings and high disk I/O costs in out-of-core settings.

By Pranjal Naman, Yogesh Simmhan

rag efficiency

arXiv Machine Learning

Jul 7

FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

arXiv:2607. 05095v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting.

By Yushu Cai, Qingrui Zhu, Lei Liu, Kai Sheng, Hao Chen, Xin He

benchmarks

arXiv Machine Learning

Jul 22

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs

arXiv:2607. 18412v1 Announce Type: new Abstract: Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems.

By Huizhe Zhang, Yuchang Zhu, Huazhen Zhong, Liang Chen, Zibin Zheng

rag

Related stories

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

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

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

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FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs