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 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

The Spectral Clustering Based on the Most Similar Relation Diagram

2011 ◽  
Vol 403-408 ◽  
pp. 2577-2580
Author(s):  
Wei Hong Xu ◽  
Min Zhu ◽  
Ya Ruo Jiang ◽  
Yu Shan Bai ◽  
Yan Yu
Keyword(s):  
Adjacency Matrix ◽  
Spectral Clustering ◽  
Clustering Method ◽  
Similar Relation ◽  
Spectral Algorithm ◽  
Balanced Partition ◽  
Artificial Datasets ◽  
Spectral Clustering Method

In this paper we present a spectral clustering method based on the MSRD (Most Similar Relation Diagram). The feature of this method is that both the constructing of the adjacency matrix and the clustering are achieved by spectral algorithm. Experiment on an artificial datasets demonstrate that our method can generate balanced partition and detect the manifold clusters no matter the unnormalized or normalized Laplassian is used and can generate partitions with different features if different MSRD is used. Experiments on some real datasets proved that our method is valid and effective.

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.

Extending Proximity Measures to Attributed Networks for Community Detection

2021 ◽  
Vol 30 (4) ◽  
pp. 441-455
Author(s):  
Rinat Aynulin ◽  
◽  
Pavel Chebotarev ◽  
◽  
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Network Structure ◽  
Real World ◽  
Adjacency Matrix ◽  
Attribute Information ◽  
Node Attributes ◽  
Proximity Measures ◽  
Definition Of ◽  
Attributed Networks

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

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.

Detecting communities in networks using a Bayesian nonparametric method

2014 ◽  
Vol 28 (28) ◽  
pp. 1450199
Author(s):  
Shengze Hu ◽  
Zhenwen Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Generative Models ◽  
Detection Methods ◽  
Model Parameters ◽  
Nonparametric Method ◽  
Bayesian Nonparametric ◽  
New Community

In the real world, a large amount of systems can be described by networks where nodes represent entities and edges the interconnections between them. Community structure in networks is one of the interesting properties revealed in the study of networks. Many methods have been developed to extract communities from networks using the generative models which give the probability of generating networks based on some assumption about the communities. However, many generative models require setting the number of communities in the network. The methods based on such models are lack of practicality, because the number of communities is unknown before determining the communities. In this paper, the Bayesian nonparametric method is used to develop a new community detection method. First, a generative model is built to give the probability of generating the network and its communities. Next, the model parameters and the number of communities are calculated by fitting the model to the actual network. Finally, the communities in the network can be determined using the model parameters. In the experiments, we apply the proposed method to the synthetic and real-world networks, comparing with some other community detection methods. The experimental results show that the proposed method is efficient to detect communities in networks.

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