Results 11 to 20 of about 146,643 (141)
Multi-view Attributed Graph Clustering Based on Contrast Consensus Graph Learning [PDF]
Jisuanji kexueMulti-view attribute graph clustering can divide nodes of graph data with multiple views into different clusters,which has attracted widespread attention from researchers in recent years.At present,many multi-view attribute graph clustering me-thods ...
LIU Pengyi, HU Jie, WANG Hongjun, PENG Bo
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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)
Yang, Xihong +8 more
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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 +7 more
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Multi-view Graph Clustering Algorithm Based on Dual Contrastive Learning and Hard Sample Mining [PDF]
Jisuanji gongchengAs 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
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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.
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Randomized graph cluster randomization
Journal of Causal Inference, 2023 Abstract The global average treatment effect (GATE) is a primary quantity of interest in the study of causal inference under network interference. With a correctly specified exposure model of the interference, the Horvitz–Thompson (HT) and Hájek estimators of the GATE are unbiased and consistent, respectively, yet known to exhibit ...
Ugander Johan, Yin Hao
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Graph Contrastive Clustering [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, these methods do not take the category information and clustering objective into consideration, thus the learned representations are not optimal for clustering and the ...
Zhong, Huasong +7 more
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Graph-based data clustering via multiscale community detection
Applied Network Science, 2020 We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation
Zijing Liu, Mauricio Barahona
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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 +3 more
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Graph Clustering with Graph Neural Networks
, 2020 Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs.
Tsitsulin, Anton +3 more
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