scholarly journals A multi-similarity spectral clustering method for community detection in dynamic networks

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Xuanmei Qin ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang ◽  
Ning Yuan
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Dynamic Networks ◽  
Clustering Method ◽  
Spectral Clustering Method

A signal-diffusion-based spectral clustering method for community detection

2019 ◽  
Vol 18 (01) ◽  
pp. 1941019
Author(s):  
Zheng Qiong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Adjacency Matrix ◽  
Spectral Clustering ◽  
Time Complexity ◽  
Detection Method ◽  
Signal Transmission ◽  
Transmission Mechanism ◽  
Clustering Method ◽  
Spectral Clustering Method

As the traditional spectral community detection method uses adjacency matrix for clustering which might cause the problem of accuracy reduction, we proposed a signal-diffusion-based spectral clustering for community detection. This method solves the problem that unfixed total signal as using the signal transmission mechanism, provides optimization of algorithm time complexity, improves the performance of spectral clustering with construction of Laplacian based on signal diffusion. Experiments prove that the method reaches as better performance on real-world network and Lancichinetti–Fortunato–Radicchi (LFR) benchmark.

scholarly journals Social Network Community Detection Using Agglomerative Spectral Clustering

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ulzii-Utas Narantsatsralt ◽  
Sanggil Kang
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithms ◽  
Real Life ◽  
Clustering Method ◽  
Network Community ◽  
Improved Performance ◽  
Spectral Clustering Method

Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Global spectral clustering in dynamic networks

2018 ◽  
Vol 115 (5) ◽  
pp. 927-932 ◽  
Author(s):  
Fuchen Liu ◽  
David Choi ◽  
Lu Xie ◽  
Kathryn Roeder
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Signal To Noise Ratio ◽  
Dynamic Networks ◽  
Brain Regions ◽  
Tuning Parameter ◽  
Expression Data ◽  
Dynamic Community Detection ◽  
Dynamic Community ◽  
Better Than

Community detection is challenging when the network structure is estimated with uncertainty. Dynamic networks present additional challenges but also add information across time periods. We propose a global community detection method, persistent communities by eigenvector smoothing (PisCES), that combines information across a series of networks, longitudinally, to strengthen the inference for each period. Our method is derived from evolutionary spectral clustering and degree correction methods. Data-driven solutions to the problem of tuning parameter selection are provided. In simulations we find that PisCES performs better than competing methods designed for a low signal-to-noise ratio. Recently obtained gene expression data from rhesus monkey brains provide samples from finely partitioned brain regions over a broad time span including pre- and postnatal periods. Of interest is how gene communities develop over space and time; however, once the data are divided into homogeneous spatial and temporal periods, sample sizes are very small, making inference quite challenging. Applying PisCES to medial prefrontal cortex in monkey rhesus brains from near conception to adulthood reveals dense communities that persist, merge, and diverge over time and others that are loosely organized and short lived, illustrating how dynamic community detection can yield interesting insights into processes such as brain development.

scholarly journals Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

Fluids ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 184
Author(s):  
Guilherme S. Vieira ◽  
Irina I. Rypina ◽  
Michael R. Allshouse
Keyword(s):  
Spectral Clustering ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Material Transport ◽  
Ensemble Forecasts ◽  
Uncertainty Model ◽  
Ocean Flows ◽  
Ocean Forecasting ◽  
High Uncertainty ◽  
Spectral Clustering Method

Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced spanning a range of potential outcomes, and trajectory clustering is used to identify robust features and quantify the uncertainty of the ensemble-averaged results. First, ensemble statistics are used to investigate the cluster sensitivity to the spectral clustering method free-parameters and the forecast parameters for the analytic Bickley jet, a geostrophic flow model. Then, we analyze an operational coastal ocean ensemble forecast and compare the clustering results to drifter trajectories south of Martha’s Vineyard. This approach identifies regions of low uncertainty where drifters released within a cluster predominantly remain there throughout the window of analysis. Drifters released in regions of high uncertainty tend to either enter neighboring clusters or deviate from all predicted outcomes.

scholarly journals Analisis Clustering Virus MERS-CoV Menggunakan Metode Spectral Clustering Dan Algoritma K-Means

2021 ◽  
Vol 5 (3) ◽  
pp. 315
Author(s):  
Septian Wulandari ◽  
Dian Novita
Keyword(s):  
Dna Sequences ◽  
Spectral Clustering ◽  
Complex Structure ◽  
World Health ◽  
Acute Respiratory Disease ◽  
Clustering Method ◽  
Grouping Method ◽  
Quantitative Descriptive ◽  
Partition Clustering ◽  
Spectral Clustering Method

<p><em>The MERS-Cov virus has spread to other countries outside Saudi Arabia. This is because the MERS-CoV virus can mutate rapidly so it is feared that it could threaten public health and even world health. This virus develops and becomes an acute respiratory disease and the mortality rate reaches 30% among 536 cases. One way to classify the MERS-CoV virus is by grouping the DNA sequences of the MERS-CoV virus which have similar characteristics and functions. Spectral clustering is a grouping method that can identify DNA gene expression. This method is also able to partition DNA data with a more complex structure than the partition clustering method. The purpose of this study was to analyze the MERS-CoV virus clustering using the spectral clustering method and the k-means algorithm. This study used a quantitative descriptive literature approach. The results showed that the results of clustering using the spectral clustering method and the k-means algorithm produced three clusters and were more homogeneous than clustering using k-means only.</em></p>

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