Results 21 to 30 of about 178,314 (358)

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Frontiers in Genetics, 2021
It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data.
Jian Liu, Jian Liu, Shuguang Ge, Shuguang Ge, Yuhu Cheng, Yuhu Cheng, Xuesong Wang, Xuesong Wang   +7 more
doaj   +1 more source

Random Graphs with Clustering [PDF]

Physical Review Letters, 2009
We offer a solution to a long-standing problem in the physics of networks, the creation of a plausible, solvable model of a network that displays clustering or transitivity -- the propensity for two neighbors of a network node also to be neighbors of one another.
openaire   +3 more sources

Trio-based collaborative multi-view graph clustering with multiple constraints

, 2021
Multi-view graph clustering is an attentional research topic in recent years due to its wide applications. According to recent surveys, most existing works focus on incorporating comprehensive information among multiple views to achieve the clustering ...
Li, Lin, Dong, Xiao, Liu, Peiyu, Tao, Xiaohui, Wang, Peipei, Wang, Ru   +5 more
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Multi-view Graph Clustering Algorithm Based on Dual Contrastive Learning and Hard Sample Mining [PDF]

Jisuanji gongcheng
As a key research direction in the field of graph mining, graph clustering aims to discover substructures or node groups with similarities from graph data and classify them into the same cluster.
QIAN Lifeng, LI Jing, ZOU Xuxi, CHEN Yu, GU Yalin, WEI Xunhu
doaj   +1 more source

Node-attribute graph layout for small-world networks [PDF]

, 2011
Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs,
Joe Faith, Helen Gibson, Gibson, Helen, Faith, Joe   +3 more
core   +1 more source

Planarization of Clustered Graphs [PDF]

, 2002
We propose a planarization algorithm for clustered graphs and experimentally test its efficiency and effectiveness. Further, we integrate our planarization strategy into a complete topology-shape-metrics algorithm for drawing clustered graphs in the orthogonal drawing convention.
Di Battista G., DIDIMO, WALTER, Marcandalli A.   +2 more
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Graph Distances and Clustering

CoRR, 2020
11 pages.
Pierre Miasnikof, Alexander Y. Shestopaloff, Leonidas S. Pitsoulis, Yuri A. Lawryshyn   +3 more
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Clustering with Multiple Graphs [PDF]

2009 Ninth IEEE International Conference on Data Mining, 2009
In graph-based learning models, entities are often represented as vertices in an undirected graph with weighted edges describing the relationships between entities. In many real-world applications, however, entities are often associated with relations of different types and/or from different sources, which can be well captured by multiple undirected ...
Wei Tang, Zhengdong Lu, Inderjit S. Dhillon   +2 more
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Clustering Powers of Sparse Graphs [PDF]

The Electronic Journal of Combinatorics, 2020
We prove that if $G$ is a sparse graph — it belongs to a fixed class of bounded expansion $\mathcal{C}$ — and $d\in \mathbb{N}$ is fixed, then the $d$th power of $G$ can be partitioned into cliques so that contracting each of these clique to a single vertex again yields a sparse graph.
Nešetřil, Jaroslav, Ossona de Mendez, Patrice, Pilipczuk, Michał, Zhu, Xuding   +3 more
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Adaptive Graph Convolution Using Heat Kernel for Attributed Graph Clustering

Applied Sciences, 2020
Attributed graphs contain a lot of node features and structural relationships, and how to utilize their inherent information sufficiently to improve graph clustering performance has attracted much attention.
Danyang Zhu, Shudong Chen, Xiuhui Ma, Rong Du   +3 more
doaj   +1 more source