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Identifying batch-integrated domains from spatial transcriptomics via graph autoencoder with contrastive learning based on cross-modality and data augmentation. [PDF]

Brief Bioinform
Mao Y, Quan L, Chen X, Wang S, Zhang G, Li S, Jiang Y, Nie L, Wu T, Meng L, Lyu Q.   +10 more
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Fuzzy graph clustering

Information Sciences, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Peng 0001, Xin Zhu, Feiping Nie 0001, Wanzeng Kong, Yuan Ge   +4 more
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Graph Clustering With Graph Capsule Network

Neural Computation, 2022
AbstractGraph 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, Jie Mu, Han Liu 0008, Xiaotong Zhang 0003, Linlin Zong, Guanglu Wang   +5 more
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Clustering Streaming Graphs

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, Rohit Khandekar, Kun-Lung Wu   +2 more
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Graph clustering

Computer Science Review, 2007
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), 2016
In 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, Haolan Zhang 0001, Ke Huang, Changbin Yu, Honglei Cui   +4 more
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Vienna Graph Clustering

2019
This paper serves as a user guide to the Vienna graph clustering framework. We review our general memetic algorithm, VieClus, to tackle the graph clustering problem. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques.
Biedermann, Sonja, Henzinger, Monika, Schulz, Christian, Schuster, Bernhard   +3 more
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Planarity for clustered graphs

1995
In this paper, we introduce a new graph model known as clustered graphs, i.e. graphs with recursive clustering structures. This graph model has many applications in informational and mathematical sciences. In particular, we study C-planarity of clustered graphs.
Qing-Wen Feng, Robert F. Cohen, Peter Eades   +2 more
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Median graph computation for graph clustering

Soft Computing, 2005
In this paper, we are interested in the problem of graph clustering. We propose a new algorithm for computing the median of a set of graphs. The concept of median allows the extension of conventional algorithms such as the k-means to graph clustering, helping to bridge the gap between statistical and structural approaches to pattern recognition ...
Adel Hlaoui, Shengrui Wang
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