arXiv AI

Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization

arXiv:2607. 11577v1 Announce Type: cross Abstract: We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model.

arXiv Machine Learning

Jul 28

Unsupervised Graph Representation Learning with Complementary View Alignment

arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.

By Zengyi Wo, Shiyu Zhang, Qiyao Peng, Tianpeng Li, Xuan Guo

rag benchmarks safety

arXiv Machine Learning

Jun 15

Generalizing GNNs with Tokenized Mixture of Experts

arXiv:2602. 09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations.

By Xiaoguang Guo, Zehong Wang, Jiazheng Li, Shawn Spitzel, Qi Yang, Kaize Ding, Jundong Li, Chuxu Zhang

efficiency benchmarks

arXiv Machine Learning

Jul 7

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.

By Abdullah Shaik, Anwar Said

rag benchmarks

arXiv Machine Learning

Jun 2

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.

By Lei Zhang, Fubo Sun, Haipeng Yang, Zhong Guan, Likang Wu

arXiv AI

Jul 23

AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation

arXiv:2602. 17071v3 Announce Type: replace-cross Abstract: Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies.

By Rong Fu, Muge Qi, Chunlei Meng, Shuo Yin, Kun Liu, Simon Fong

llms safety

arXiv Machine Learning

Jun 4

Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning

arXiv:2606. 05046v1 Announce Type: new Abstract: We introduce Graph Cascades, a mesoscopic rewiring strategy for Graph Neural Networks (GNNs) and Graph Transformers (GTs) that captures intermediate-scale graph structure beyond purely local edges or fully global attention.

By Meher Chaitanya, My Le, Luana Ruiz

llms diffusion benchmarks

arXiv AI

Jun 24

Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings

arXiv:2505. 13087v2 Announce Type: replace-cross Abstract: We propose a novel benchmarking methodology for graph neural networks (GNNs) based on the graph alignment problem, a combinatorial optimization task that generalizes graph isomorphism by aligning two unlabeled graphs to maximize overlapping edges.

By Adrien Lagesse, Marc Lelarge

llms rag benchmarks safety

arXiv Machine Learning

Jul 31

TopoFormer: Topology Meets Attention for Graph Learning

arXiv:2607. 28259v1 Announce Type: new Abstract: We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences.

By Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora, Baris Coskunuzer

llms rag benchmarks

arXiv Machine Learning

Jun 16

Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs

arXiv:2602. 10031v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine learning and signal processing.

By Antonis Vasileiou, Juan Cervino, Pascal Frossard, Charilaos I. Kanatsoulis, Christopher Morris, Michael T. Schaub, Pierre Vandergheynst, Zhiyang Wang, Guy Wolf, Ron Levie

arXiv AI

Aug 6

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

arXiv:2608. 04381v1 Announce Type: cross Abstract: Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space.

By Tinghe Zhang, Jian Xu, Jiaheng Chen, Jiaxing Li, Yucheng Xiao, Qiang Wang

rag diffusion fine-tuning benchmarks

arXiv Machine Learning

Jul 2

SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport

arXiv:2607. 00377v1 Announce Type: new Abstract: Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention.

By Yuting Zhang, Yanbei Liu, Zhitao Xiao, Lei Geng, Yanwei Pang, Xiao Wang

rag efficiency benchmarks

arXiv Machine Learning

Jul 21

Node4All: Learning Node Representation Beyond Datasets

arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.

By Dooho Lee, Jaemin Yoo

llms benchmarks

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