Results 111 to 120 of about 146,643 (141)

A null findings study: graph theoretical analysis of the fetal functional connectome shows no relationships with future autistic traits. [PDF]

Neuroimage Rep
Chen B, Ji L, Menu I, Taylor A, Trentacosta CJ, Thomason ME.   +5 more
europepmc   +1 more source

Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation. [PDF]

Hum Brain Mapp
Liu Y, Li J, Wisnowski JL, Leahy RM.
europepmc   +1 more source

Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness. [PDF]

Brief Bioinform
Ding Y, Jiang Y, Wang J, Tan D, Su Y, Zheng C.   +5 more
europepmc   +1 more source

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Graph Clustering With Graph Capsule Network

Neural Computation, 2022
AbstractGraph clustering, which aims to partition a set of graphs into groups with similar structures, is a fundamental task in data analysis. With the great advances made by deep learning, deep graph clustering methods have achieved success. However, these methods have two limitations: (1) they learn graph embeddings by a neural language model that ...
Xianchao, Zhang, Jie, Mu, Han, Liu, Xiaotong, Zhang, Linlin, Zong, Guanglu, Wang   +5 more
openaire   +2 more sources

Discrete Multi-Graph Clustering

IEEE Transactions on Image Processing, 2019
Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfortunately, directly optimizing the spectral clustering inevitably results in an NP-hard problem due to the discrete constraints on the clustering labels ...
Minnan Luo, Caixia Yan, Qinghua Zheng, Xiaojun Chang, Ling Chen, Feiping Nie   +5 more
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Vienna Graph Clustering

2019
This paper serves as a user guide to the Vienna graph clustering framework. We review our general memetic algorithm, VieClus, to tackle the graph clustering problem. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques.
Biedermann, Sonja, Henzinger, Monika, Schulz, Christian, Schuster, Bernhard   +3 more
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Embedding Graph Auto-Encoder for Graph Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2023
Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative ability, several graph auto-encoder (GAE) models, which are based on semisupervised graph convolution networks (GCN), have been developed and they have achieved impressive results
Hongyuan Zhang, Pei Li, Rui Zhang, Xuelong Li   +3 more
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Robust Structured Graph Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2020
Graph-based clustering methods have achieved remarkable performance by partitioning the data samples into disjoint groups with the similarity graph that characterizes the sample relations. Nevertheless, their learning scheme still suffers from two important problems: 1) the similarity graph directly constructed from the raw features may be unreliable ...
Dan Shi, Lei Zhu, Yikun Li, Jingjing Li, Xiushan Nie   +4 more
openaire   +2 more sources