arXiv Machine Learning

Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework

arXiv:2607. 16728v1 Announce Type: new Abstract: The Broad Learning System (BLS) has been widely used for data classification and is based on a layer-by-layer feed-forward structure.

arXiv Machine Learning

Jul 8

Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

arXiv:2607. 05635v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities.

By Vrushank Ahire, Yogesh Kumar, M. A. Ganaie

rag benchmarks

arXiv Machine Learning

Jul 28

XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss

arXiv:2607. 23149v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification.

By Yogesh Kumar, Mudasir Ganaie

rag benchmarks

arXiv AI

Jun 17

Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation

arXiv:2606. 17406v1 Announce Type: cross Abstract: Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes.

By Marina Chagas Bulach Gapski, Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio, Lucas Pascotti Valem, Daniel Carlos Guimar\~aes Pedronette, Mohand Said Allili

llms computer-vision

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 Machine Learning

Jun 3

HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks

arXiv:2606. 03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge.

By Zongrui Li, Yuhang Zhao, Ying Zhao, Yuanzhao Guo, Qiang Huang, Yuan Tian

safety

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 8

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection

arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.

By Xudong Wang, Ziheng Sun, Chris Ding, Jicong Fan

benchmarks

arXiv AI

Jul 10

Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph

arXiv:2607. 08746v1 Announce Type: cross Abstract: While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally.

By Duen Horng Chau, Donghao Ren, Fred Hohman, Dominik Moritz

rag

arXiv AI

Jun 2

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

arXiv:2606. 01560v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs.

By Canyixing Cui, Tao Wu, Xingping Xian, Xiao-Ke Xu, Mao Wang, Weina Niu

rag safety

arXiv AI

Aug 7

Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis

arXiv:2608. 06037v1 Announce Type: new Abstract: Relational inductive biases are essential for capturing structural dependencies among data.

By Rafa{\l} Buler (Gda\'nsk University of Technology), Jakub Buler (Gda\'nsk University of Technology), Maciej Bobowicz (Medical University of Gda\'nsk), Micha{\l} Grochowski (Gda\'nsk University of Technology)

rag computer-vision benchmarks

arXiv AI

Jun 2

On the Theoretical Limitations of Embedding-based Link Prediction

arXiv:2506. 22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces.

By Samy Badreddine, Emile van Krieken, Luciano Serafini

rag

arXiv Machine Learning

Jun 5

The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning

arXiv:2606. 06397v1 Announce Type: new Abstract: Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure.

By Shuo Wang, Xiangyu Wang, Quanxin Wang, Bailin Wu, Bokui Wang, Shunyang Huang, Boyan Deng, Haonan Liu, Ruiyi Fang, Zhenxiang Xu, Boyu Wang, Zhao Kang

benchmarks safety

Related stories