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Multi-objective evolutionary clustering for large-scale dynamic community detection
Information Sciences, 2021The research of dynamic community detection is becoming increasingly popular since it can disclose how the community structures change over time in dynamic networks.
Ying Yin +3 more
semanticscholar +1 more source
Enterprise Community Detection
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Employees in companies can be divided into different social communities, and those who frequently socialize with each other are treated as close friends and will be grouped in the same community. In the enterprise context, a large amount of information about the employees is available in both (1) offline company internal sources and (2) online ...
Jiawei Zhang 0001 +2 more
openaire +1 more source
Swarm and Evolutionary Computation, 2021
Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem.
B. Attea +6 more
semanticscholar +1 more source
Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem.
B. Attea +6 more
semanticscholar +1 more source
A Community Structure Enhancement-Based Community Detection Algorithm for Complex Networks
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations.
Yansen Su, Yunyun Niu, Fan Cheng
exaly +2 more sources
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks.
Xuanyu Cao, Yan Chen 0007, K. J. Ray Liu
openaire +1 more source
Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks.
Xuanyu Cao, Yan Chen 0007, K. J. Ray Liu
openaire +1 more source
A Community Detection Method for Social Network Based on Community Embedding
IEEE Transactions on Computational Social Systems, 2021Most community detection methods focus on the similarities between detection nodes to achieve community partitioning. Traditional network representation learning methods are also limited to the local context of the central nodes, which results in less ...
Meizi Li +4 more
semanticscholar +1 more source
Symmetry and Graph Bi-Regularized Non-Negative Matrix Factorization for Precise Community Detection
IEEE Transactions on Automation Science and EngineeringCommunity is a fundamental and highly desired pattern in a Large-scale Undirected Network (LUN). Community detection is a vital issue when LUN representation learning is performed.
Zhigang Liu, Xin Luo, Mengchu Zhou
semanticscholar +1 more source
, 2021
Community detection aims to discover and reveal community structures in complex networks. Some community detection method is called local methods that only apply local information in discovering steps.
Saeid Aghaalizadeh +3 more
semanticscholar +1 more source
Community detection aims to discover and reveal community structures in complex networks. Some community detection method is called local methods that only apply local information in discovering steps.
Saeid Aghaalizadeh +3 more
semanticscholar +1 more source
2013
We propose an algorithm for the detection of communities in networks. The algorithm exploits degree and clustering coefficient of vertices as these metrics characterize dense connections, which, we hypothesize, are indicative of communities. Each vertex, independently, seeks the community to which it belongs by visiting its neighbour vertices and ...
Yi Song 0005, Stéphane Bressan
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We propose an algorithm for the detection of communities in networks. The algorithm exploits degree and clustering coefficient of vertices as these metrics characterize dense connections, which, we hypothesize, are indicative of communities. Each vertex, independently, seeks the community to which it belongs by visiting its neighbour vertices and ...
Yi Song 0005, Stéphane Bressan
openaire +1 more source
IEEE Transactions on Computational Social Systems, 2021
The flow of information through active users in online social networks (OSNs) plays a major role in forming natural social groups, popularly known as communities.
Soumita Das, A. Biswas
semanticscholar +1 more source
The flow of information through active users in online social networks (OSNs) plays a major role in forming natural social groups, popularly known as communities.
Soumita Das, A. Biswas
semanticscholar +1 more source