scholarly journals Latent Space Approaches to Community Detection in Dynamic Networks

2017 ◽  
Vol 12 (2) ◽  
pp. 351-377 ◽  
Author(s):  
Daniel K. Sewell ◽  
Yuguo Chen
Keyword(s):  
Community Detection ◽  
Dynamic Networks ◽  
Latent Space

Community detection in dynamic networks with spark

2018 ◽  
Vol 3 (3) ◽  
pp. 236
Author(s):  
Priyangika R. Piyasinghe ◽  
J. Morris Chang
Keyword(s):  
Community Detection ◽  
Dynamic Networks

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 Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e86891 ◽  
Author(s):  
Rodica Ioana Lung ◽  
Camelia Chira ◽  
Anca Andreica
Keyword(s):  
Game Theory ◽  
Community Detection ◽  
Dynamic Networks ◽  
Complex Dynamic ◽  
Extremal Optimization

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.

scholarly journals Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Jingjing Ma ◽  
Jie Liu ◽  
Wenping Ma ◽  
Maoguo Gong ◽  
Licheng Jiao
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Dynamic Networks ◽  
Time Step ◽  
Current Time ◽  
Multiobjective Evolutionary Algorithm ◽  
Dynamic Social Networks

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

Community detection in dynamic networks using constraint non-negative matrix factorization

2020 ◽  
Vol 24 (1) ◽  
pp. 119-139
Author(s):  
Shuaihui Wang ◽  
Guopeng Li ◽  
Guyu Hu ◽  
Hao Wei ◽  
Yu Pan ◽  
...  
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Dynamic Networks ◽  
Non Negative Matrix Factorization

scholarly journals Identifying Communities in Dynamic Networks Using Information Dynamics

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 425
Author(s):  
Zejun Sun ◽  
Jinfang Sheng ◽  
Bin Wang ◽  
Aman Ullah ◽  
FaizaRiaz Khawaja
Keyword(s):  
Community Detection ◽  
Dynamic Networks ◽  
Parameter Tuning ◽  
Detection Algorithm ◽  
Network Evolution ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Information Dynamics ◽  
Dynamic Community Detection ◽  
Dynamic Community

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

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