scholarly journals Community detection in complex networks using density-based clustering algorithm and manifold learning

2016 ◽  
Vol 464 ◽  
pp. 221-230 ◽  
Author(s):  
Tao You ◽  
Hui-Min Cheng ◽  
Yi-Zi Ning ◽  
Ben-Chang Shia ◽  
Zhong-Yuan Zhang
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Manifold Learning ◽  
Clustering Algorithm ◽  
Density Based Clustering

scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

Detecting communities by asymmetric intimacy in directed-weighted network

2017 ◽  
Vol 28 (01) ◽  
pp. 1750006 ◽  
Author(s):  
Xingyuan Wang ◽  
Xiaomeng Qin
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Potential Application ◽  
Clustering Algorithm ◽  
Intimate Relationship ◽  
Detection Algorithm ◽  
Weighted Network ◽  
Node Clustering ◽  
Public Concerns

Community detection and analysis have attracted wide public concerns over the recent years. Meanwhile, many related algorithms in complex networks have been proposed. However, most of them concentrate on undirected and unweighted networks. Concerning the significant theoretical value and potential application foreground for directed-weighted networks, in this paper, a novel hierarchical communities detection algorithm (termed as DCBAI) has been proposed on the basis of asymmetric intimacy between nodes. Community structures are effectively detected by node clustering algorithm in directed-weighted network, and a set of optimal communities are generated. In addition, a new and asymmetric parameter is adopted to measure the intimate relationship between nodes. We make some simulation using the proposed algorithm in real-world networks and artificial networks, and the result obtained proves that the parameter can describe the direct and indirect relationships between two nodes. Eventually, comparison with similar algorithms shows that our proposed algorithm has better performance.

Deep Auto-encoded Clustering Algorithm for Community Detection in Complex Networks

2019 ◽  
Vol 28 (3) ◽  
pp. 489-496 ◽  
Author(s):  
Feifan Wang ◽  
Baihai Zhang ◽  
Senchun Chai
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Clustering Algorithm

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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