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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

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Conference Open access Sep 2026

ADC-GNN: Adaptive Dual-level Collaborative Graph Neural Networks for Graph Classification

This work proposes a novel Adaptive Dual-level Collaborative GNN associated with an adaptive dual-level collaborative mechanism, and shows that the ADC-GNN can inject the learned high-level information back into the node level, forming a closed-loop, bidirectional optimization process.

Wan Tang, Lu Bai, Li-Xin Cui et al. · 0 citations
Preprint Aug 2026

Two-level domain-decomposition AdaGrad method for scalable training of graph neural networks

The proposed DD-AG2m alternates between AG2m optimization on the original (global) graph and AG2m optimization on the partitioned graphs, and introduces a two-level variant that performs global optimization steps on a coarse graph obtained by randomly subsampling nodes within each subdomain.

Laurynas Varnas, Julien Herrmann, Alexander Heinlein et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Geometric Flow enhanced Graph Coarsening

This study introduces discrete graph curvature, particularly Ollivier-Ricci curvature, as a measure of higher-order connectivity around an edge, and introduces a novel graph pooling method named RicciPool, utilizing the spectral clustering technique to learn a new cluster assignment matrix.

Chao-Qun Fei, Guo-Xuan Li, Ting-Lve Zhou et al. · 1 citation
Sep 2026

MvCConvFormer: The graph convolution transformer network with self-distillation for multi-view graph clustering

An innovative method for multi-view graph clustering named the graph convolution Transformer network with self-distillation (MvCConvFormer), which applies the Transformer architecture to effectively fuse the structural information of the optimal adjacency matrix and the node representations learned by the pre-training...

Jin-Ke Wang, Xue-Fang Zhang, Chun-Chun Zhang et al. · 0 citations
#machine learning Preprint Oct 2026

Stability-Shaped Deep Graph Learning

A unified, mode-wise stability framework for deep GNN propagation that provides a principled characterization of over-smoothing is developed and Stability-Shaped Deep Graph Learning (SDGL) is proposed to mitigate over-smoothing in deep GNNs.

Jun-You Zhu, Lang-Zhou He, Fen-Ying Cai et al. · 0 citations
Sep 2026

RCGELM-AE: an efficient graph embedding deep model without gradient descent

This study proposes a model called the Residual compensation graph Convolutional Generalized Extreme Learning Machine Autoencoder (RCGELM-AE), which is built on the Extreme Learning Machine Autoencoder (ELM-AE) and incorporates two key designs, and offers a novel, gradient-descent-free solution for achieving efficient...

Xin-Yi Lin, Xiao-Yun Chen, Shu-Lan Zheng et al. · 0 citations

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Microsoft Research Blog Jul 13, 2026

Verifying Rust cryptography in SymCrypt, from standards to code

Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.

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