arXiv Machine Learning June 2, 2026 By Ali Azizpour, Reza Ramezanpour, Santiago Segarra

From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning

Read the original on arXiv Machine Learning →

arXiv:2510. 03690v4 Announce Type: replace Abstract: Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions.

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

benchmarks

arXiv Machine Learning

Jun 10

Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

arXiv:2606. 10284v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm.

By Lianze Shan, Ningchong Wang, Jitao Zhao, Di Jin, Dongxiao He

arXiv Machine Learning

Jun 19

Toward General Digraph Contrastive Learning: A Dual Spatial Perspective

arXiv:2510. 16311v3 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information.

By Zhengyu Wu, Daohan Su, Yang Zhang, Xunkai Li, Rong-Hua Li, Guoren Wang

benchmarks

arXiv AI

Jul 8

Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy

arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.

By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu

benchmarks

arXiv AI

Jun 11

From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

arXiv:2606. 11831v1 Announce Type: cross Abstract: Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges.

By Qi Shao, Hao Guo, Jiawen Chen, Duxin Chen, Wenwu Yu

diffusion benchmarks

arXiv Machine Learning

Jun 17

SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs

arXiv:2606. 12867v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities.

By Zhengyu Wu, Xu Wang, Hongchao Qin, Xunkai Li, Guang Zeng, Rong-Hua Li, Guoren Wang

multimodal benchmarks safety

arXiv Machine Learning

Jul 22

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

llms rag multimodal benchmarks

Related stories

Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

Toward General Digraph Contrastive Learning: A Dual Spatial Perspective

Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy

From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models