Results 221 to 230 of about 156,516 (263)
GOLDEN fusion: a graph-oriented learning with domain-embedding network fusion for generating super gene sets in functional genomics. [PDF]
Brief BioinformLi Q +5 more
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scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data. [PDF]
BMC BiolWang J +5 more
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New algorithms for unsupervised cell clustering from scRNA-seq data. [PDF]
Bioinform AdvRobles M +11 more
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Longitudinal Study of Adolescent Brain Connectivity Development Using Sign-Aware Graph Theory Metrics. [PDF]
Hum Brain MappViswanathan S +3 more
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2012 IEEE 32nd International Conference on Distributed Computing Systems, 2012
In this paper, we propose techniques for clustering large-scale "streaming" graphs where the updates to a graph are given in form of a stream of vertex or edge additions and deletions. Our algorithm handles such updates in an online and incremental manner and it can be easily parallel zed.
Ahmed Eldawy +2 more
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In this paper, we propose techniques for clustering large-scale "streaming" graphs where the updates to a graph are given in form of a stream of vertex or edge additions and deletions. Our algorithm handles such updates in an online and incremental manner and it can be easily parallel zed.
Ahmed Eldawy +2 more
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Information Sciences, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Peng 0001 +4 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Peng 0001 +4 more
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Graph Clustering With Graph Capsule Network
Neural Computation, 2022AbstractGraph 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 0001 +5 more
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Computer Science Review, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Clustering by Creating a Graph
2016 12th International Conference on Computational Intelligence and Security (CIS), 2016In this paper, we presented a novel graph-based clustering algorithm (GC). GC contains two main steps: the first step is to create a graph and find out the key nodes as centers, the second step is to divide every data point to each center. The centers are selected from a graph view.
Yiwen Wang +4 more
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