Spectral embedding network for attributed graph clustering

Zhang, Xiaolin; Liu, Han; Wu, Xiao-Ming; Zhang, Xianchao

doi:10.1016/j.neunet.2021.05.026

Cited by 41 publications

(17 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If no cluster number is specified, COMMO will automatically determine the number of clusters using the k-nearest neighbor algorithm (see Supplementary Section S1.1 ). Second, eight clustering methods are used to identify gene modules, including FLAME (Fuzzy clustering by Local Approximation of Memberships) ( Fu and Medico 2007 ), K-means ( Timmerman et al 2013 ), SOM (self-organizing mapping) ( Wang et al 2002 ), spectral clustering ( Zhang et al 2021 ), Agglomerative ( Liu et al 2022b ), Hclust ( Bu et al 2022 ), NMF (non-negative matrix factorization) ( Liefeld et al 2023 ), and ICA (independent component analysis) ( Hyvärinen 2013 ). Detailed descriptions of these eight methods can be found in the Supplementary Materials .…”

Section: Methodsmentioning

confidence: 99%

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods

Wu,

Han,

Song

et al. 2023

Bioinformatics

View full text Add to dashboard Cite

Summary A variety of computational methods have been developed to identify functionally related gene modules from genome-wide gene expression profiles. Integrating the results of these methods to identify consensus modules is a promising approach to produce more accurate and robust results. In this application note, we introduce COMMO, the first web server to identify and analyze consensus gene functionally related gene modules from different module detection methods. First, COMMO implements eight state-of-the-art module detection methods and two consensus clustering algorithms. Second, COMMO provides users with mRNA and protein expression data for 33 cancer types from three public databases. Users can also upload their own data for module detection. Third, users can perform functional enrichment and two types of survival analyses on the observed gene modules. Finally, COMMO provides interactive, customizable visualizations and exportable results. With its extensive analysis and interactive capabilities, COMMO offers a user-friendly solution for conducting module-based precision medicine research. Availability and implementation COMMO web is available at https://commo.ncpsb.org.cn/, with the source code available on GitHub: https://github.com/Song-xinyu/COMMO/tree/master.

show abstract

Section: Methodsmentioning

confidence: 99%

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods

Wu,

Han,

Song

et al. 2023

Bioinformatics

View full text Add to dashboard Cite

show abstract

“…DAEGC [8] optimizes graph reconstruction loss and a clustering loss jointly. SENet [46] uses a spectral clustering loss to learn node embeddings. GC-VGE [37] introduces a joint framework for clustering and representation learning by utilizing a variational graph embedding mechanism.…”

Section: B Baseline Methodsmentioning

confidence: 99%

Deep Graph Clustering via Mutual Information Maximization and Mixture Model

Ahmadi¹,

Safayani²,

Mirzaei³

2022

Preprint

View full text Add to dashboard Cite

Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. In this paper, we introduce a contrastive learning framework for learning clustering-friendly node embedding. Although graph contrastive learning has shown outstanding performance in self-supervised graph learning, using it for graph clustering is not well explored. We propose Gaussian mixture information maximization (GMIM) which utilizes a mutual information maximization approach for node embedding. Meanwhile, it assumes that the representation space follows a Mixture of Gaussians (MoG) distribution. The clustering part of our objective tries to fit a Gaussian distribution to each community. The node embedding is jointly optimized with the parameters of MoG in a unified framework. Experiments on real-world datasets demonstrate the effectiveness of our method in community detection.

show abstract

“…Influenced by deep learning, graph representation learning is receiving increasing attention from researchers. For example, SENET [12] improved graph structure by fusing common neighbor nodes and aggregated multi-order node features and structural features using kernel functions. LGNN [13] introduced supervised learning into multi-scale graph representation learning.…”

Section: Related Work 21 Graph Representation Learningmentioning

confidence: 99%

Hypergraph network embedding for community detection

Xiang,

You,

Wang

et al. 2024

J Supercomput

View full text Add to dashboard Cite

Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in sub-optimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higherorder correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE.

show abstract

Spectral embedding network for attributed graph clustering

Cited by 41 publications

References 59 publications

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods

Deep Graph Clustering via Mutual Information Maximization and Mixture Model

Hypergraph network embedding for community detection

Contact Info

Product

Resources

About