arXiv AI June 30, 2026 By Shuyin Xia, Guoyin Wang, Xinbo Gao, Xiaoyu Lian, Hongzhi Kuai

Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method

arXiv:2304. 11171v5 Announce Type: replace-cross Abstract: To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed granular-ball computing as a new artificial intelligence learning paradigm.

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

arXiv Machine Learning

Jul 31

MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering

arXiv:2605. 08759v3 Announce Type: replace Abstract: Existing granular-ball generation methods are still mainly driven by handcrafted quality measures and heuristic splitting or stopping criteria, which may weaken the transparency of local generation decisions in clustering.

By Zeqiang Xian, Caihui Liu, Yong Zhang, Wenjing Qiu, Duoqian Miao, Witold Pedrycz

arXiv Machine Learning

5d ago

Adaptive $k$ Nearest Neighbors Classifier via Granular Ball Computing

arXiv:2608. 12903v1 Announce Type: new Abstract: The $k$-Nearest Neighbor~(KNN) algorithm is widely used across various tasks.

By Xiaoyu Lian, Shuyin Xia, Hongxuan He, Lifeng Shen, Guoyin Wang, Xinbo Gao

arXiv AI

Aug 3

Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction

arXiv:2607. 29115v1 Announce Type: cross Abstract: Link prediction aims to identify potential or future connections within a given graph structure.

By Sen Zhao, Cheng Liu, Shuyin Xia, Zhiyuan Liu, Yi Liu, Yi Wang, Wei Wang

rag

arXiv Machine Learning

Jun 11

Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization

arXiv:2606. 12077v1 Announce Type: new Abstract: Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.

By Yifan Wang, Lifeng Shen, Shuyin Xia, Yi Wang

benchmarks

arXiv Machine Learning

Aug 11

Machine Learning and Data Analysis Using Posets: A Survey

arXiv:2404. 03082v3 Announce Type: replace Abstract: Partially ordered sets (posets) are discrete mathematical structures that formalize the notion of comparison without forcing every pair of objects to be comparable.

By Arnauld Mesinga Mwafise

reinforcement-learning safety

arXiv AI

Jul 24

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls

arXiv:2607. 21381v1 Announce Type: new Abstract: Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph.

By Jiancu Chen, Shuyin Xia, Guan Wang, Degang Chen, Fan Chen

benchmarks

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