Non-traditional spectral clustering algorithms for the detection of community structure in complex networks: a comparative analysis

2011 ◽  
Vol 2011 (05) ◽  
pp. P05012 ◽  
Author(s):  
Xiaoke Ma ◽  
Lin Gao
Keyword(s):  
Community Structure ◽  
Comparative Analysis ◽  
Complex Networks ◽  
Spectral Clustering ◽  
Clustering Algorithms

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.

Community detection based on gravitational coefficient in collaboration network

2020 ◽  
Vol 34 (14) ◽  
pp. 2050143
Author(s):  
Wen Zhou ◽  
Shuaiqin Zhao
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Clustering Algorithms ◽  
Similarity Index ◽  
Collaborative Networks ◽  
Collaboration Network ◽  
Collaborative Relationship ◽  
Detection Algorithms ◽  
Special Complex

One important characteristic of complex networks is community structure. How to effectively divide the potential community structure of complex networks has been the focus of scholars because communities may have very different properties than the network. A community is usually defined as a collection of nodes with similar attributes. Generally, nodes in the same community are relatively densely connected to each other, compared with nodes from different communities. From the perspective of clustering, nodes in the same community can be considered as having higher similarities. Therefore, using graph clustering algorithms for community detection is theoretically feasible. Collaborative networks are special complex networks. A collaborative relationship tends to connect to multiple collaborators, which makes it hard to build collaborative networks by abstracting the collaboration into edges. Based on characteristics of the collaborative network, we expand the cluster similarity index and propose a gravitational coefficient index to measure the similarity of nodes and subsequently design community detection algorithms. Experiments using real datasets show that the proposed algorithm can obtain higher quality community partitioning results and avoid falling into local optimal solutions to obtain larger-scale communities than classical community detection algorithms.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Research on Spectral Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1350-1353
Author(s):  
Li Li Fu ◽  
Yong Li Liu ◽  
Li Jing Hao
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Theoretical Foundation ◽  
Clustering Algorithms ◽  
Spectral Graph Theory ◽  
Graph Partition ◽  
Mining Areas ◽  
Spectral Graph ◽  
Definition Of ◽  
Spectral Clustering Algorithm

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.

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