arXiv Machine Learning

Robust Graph Clustering Network for Multiple Missing Data

The paper introduces the Robust Graph Clustering Network for Multiple Missing Data (RGCN), a method designed to cluster graphs with simultaneous missing node attributes and structural links. RGCN employs a view‑decoupled dual‑branch imputation to reduce cross‑view interference, a multi‑hyperspherical mixture prior to improve cluster compactness and separability on a directional latent manifold, and a boundary‑aware contrastive enhancement objective to counteract cluster blurring caused by imputation bias. Experiments on real‑world datasets show that RGCN consistently outperforms state‑of‑the‑art baselines across various missing data patterns.

arXiv Machine Learning

Jun 9

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

arXiv:2606. 09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images.

By Zekai Chen, Miao Zhang, Jiayang Xing, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang

llms multimodal benchmarks

arXiv Machine Learning

Jul 14

SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering

arXiv:2607. 10413v1 Announce Type: cross Abstract: Prototype-based Incomplete Multi-view Clustering has recently attracted increasing attention by exploiting prototypes as semantic anchors for missing-view imputation.

By Yaoyuan Guo, Zhibin Gu, Songhe Feng, Yuhui Zheng, Bing Li

benchmarks

arXiv AI

Jul 9

Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery

arXiv:2607. 06930v1 Announce Type: cross Abstract: Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis.

By Chuyao Zhang, E Li, Taochen Chen, Yiqun Zhang, Yuzhu Ji, Shuping Zhao, Peng Liu, Yiu-ming Cheung

benchmarks safety

arXiv Machine Learning

Aug 4

RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering

arXiv:2608. 00621v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-free entity grouping tasks, including community discovery and product segmentation.

By Yinlin Zhu, Di Wu, Ziyu Han, Zekai Chenm, Wang Luo, Miao Hu, Guocong Quan

computer-vision efficiency multimodal benchmarks

arXiv Machine Learning

Jul 7

Missing Data Imputation under Manifold Hypothesis

arXiv:2607. 03641v1 Announce Type: cross Abstract: The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold.

By Zelong Bi, Amuchechukwu Ibenegbu, Sarat Moka

diffusion benchmarks

arXiv Machine Learning

Jun 16

Unsupervised Learning for Missing Modalities in Multimodal Learning

arXiv:2606. 15743v1 Announce Type: new Abstract: This paper addresses the missing-modality challenge in multi-modal learning by introducing Unsupervised Learning for Missing Modalities in Multi-Modal Learning (UL4M4), a flexible framework that imputes missing feature embeddings in a task-independent manner before supervised prediction.

By Hassan Ismkhan, Hamid Bouchahcia

rag multimodal benchmarks

arXiv Machine Learning

Sep 23

Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach

arXiv:2609.26063v1 Announce Type: new Abstract: Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a pro...

By Yinlin Zhu, Di Wu, Wang Luo, Guocong Quan, Miao Hu

benchmarks

arXiv Machine Learning

Sep 1

A Deep Latent Variable Framework for Jointly Modeling Missingness, Measurement Error, and Heterogeneity

arXiv:2608.30040v1 Announce Type: cross Abstract: Missing data, measurement error, and population heterogeneity are pervasive challenges in analyzing data arising from modern observational studies an...

By Yasin Khadem Charvadeh, Grace Y. Yi, Mithat G\"onen, Pouya Faroughi

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 Machine Learning

Jul 13

Pattern-Aware Graph Neural Networks for Handling Missing Data

arXiv:2607. 08915v1 Announce Type: new Abstract: Missing data is ubiquitous in real-world datasets.

By Minett Tran, Taehee Jeong

rag benchmarks

arXiv Machine Learning

Jun 2

Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods

arXiv:2504. 19419v3 Announce Type: replace Abstract: Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph.

By Zhaiming Shen, Sung Ha Kang

diffusion

arXiv Machine Learning

Jul 1

MNAR-$k$-means: A $k$-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability

arXiv:2606. 31253v1 Announce Type: cross Abstract: The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values.

By Xin Guan

safety

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