Results 41 to 50 of about 178,314 (358)
Fine-grained Attributed Graph Clustering
, 2022 Graph clustering is a prevalent issue associated with social networks, data mining, and machine learning; its objective is to detect communities or groups in networks.
Kang, Z, Liu, Z, Tian, L, Pan, S
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Graph Embedding via Graph Summarization
IEEE Access, 2021 Graph representation learning aims to represent the structural and semantic information of graph objects as dense real value vectors in low dimensional space by machine learning.
Jingyanning Yang +2 more
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Spectral clustering based on high‐frequency texture components for face datasets
IET Image Processing, 2021 Spectral clustering is one of the most widely used technologies for clustering tasks, which represents data as a weighted graph, and aims to find an appropriate way to cut the graph apart in order to categorize the raw data.
Zexiao Liang +3 more
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Dynamic-Fusion Multi-view Projection Clustering Algorithm [PDF]
Jisuanji kexue yu tansuo, 2023 Multi-view clustering is a hot research area, which has attracted increasing attention. Most existing multi- view clustering methods usually learn the data first, and then cluster the fused unified graph to get the final result. This two-step strategy of
JIANG Kaibin, ZHOU Shibing, QIAN Xuezhong, GUAN Jiaojiao
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Cluster-Guided Contrastive Graph Clustering Network
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1)
Xihong Yang +8 more
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PLoS Computational Biology, 2022 Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited ...
Snehalika Lall +2 more
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Graph clustering method based on structure entropy constraints
网络与信息安全学报, 2021 Aiming at the problem of how to decode the true structure of the network from the network embedded in the large-scale noise structure at the open information sharing platform centered on big data, and furthermore accurate mining results can be obtained ...
ZHANG Zhiying, TIAN Youliang
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Cluster Persistence for Weighted Graphs
Entropy, 2023 Persistent homology is a natural tool for probing the topological characteristics of weighted graphs, essentially focusing on their 0-dimensional homology. While this area has been thoroughly studied, we present a new approach to constructing a filtration for cluster analysis via persistent homology. The key advantages of the new filtration is that (a)
Omer Bobrowski, Primoz Skraba
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Sampling clustering based on multi-view attribute structural relations.
PLoS ONEIn light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes.
Guoyang Tang +3 more
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, 2007 Wittkop T, Baumbach J, Lobo FP, Rahmann S. Large scale clustering of protein sequences with FORCE - a layout based heuristic for weighted cluster editing. BMC Bioinformatics.
Wittkop, Tobias +11 more
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