Results 21 to 30 of about 156,516 (263)
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 +3 more
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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. +2 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 +2 more
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Multi-view Clustering Based on Bipartite Graph Cross-view Graph Diffusion [PDF]
Jisuanji kexueMulti-view clustering is an research hotspots in the field of unsupervised learning.Recently,the method based on cross-view graph diffusion uses the complementary information between multiple views to obtain a unified graph for clustering on the basis of
WANG Jinfu, WANG Siwei, LIANG Weixuan, YU Shengju, ZHU En
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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 +3 more
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Improved Graph Clustering [PDF]
IEEE Transactions on Information Theory, 2014 Graph clustering involves the task of dividing nodes into clusters, so that the edge density is higher within clusters as opposed to across clusters. A natural, classic and popular statistical setting for evaluating solutions to this problem is the stochastic block model, also referred to as the planted partition model.
Yudong Chen 0001 +2 more
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A Deep Graph Structured Clustering Network
IEEE Access, 2020 Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information
Xunkai Li +5 more
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Ensemble Clustering for Graphs [PDF]
, 2018 9 pages, 5 ...
Valérie Poulin, François Théberge
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Adaptive Graph Representation for Clustering
IEEE Access, 2022 Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen +5 more
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