scholarly journals Effective Semisupervised Community Detection Using Negative Information

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Dong Liu ◽  
Dequan Duan ◽  
Shikai Sui ◽  
Guojie Song
Keyword(s):  
Community Detection ◽  
Prior Information ◽  
Detection Method ◽  
Detection Algorithm ◽  
Negative Information ◽  
Discovery Process ◽  
The Past ◽  
Considerable Research ◽  
Specific Community ◽  
Community Detection Algorithm

The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.

Research on Fast Community Detection Algorithm in Microblog

2014 ◽  
Vol 513-517 ◽  
pp. 2045-2049
Author(s):  
Jie Tian ◽  
Hao Guo ◽  
Yu Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Detection Method ◽  
Heuristic Method ◽  
Computation Time ◽  
Detection Algorithm ◽  
Core Structure ◽  
Large Networks ◽  
The Core ◽  
Community Detection Algorithm

According to the problem of extracting the community structure of large networks, we propose a simple heuristic method based on community coding optimization. It is shown to outperform the InfoMap community detection method in terms of computation time. Experiments show that our method can find out various communities in microblog, which reveal the core structure of the network.

scholarly journals An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Feifan Wang ◽  
Baihai Zhang ◽  
Senchun Chai ◽  
Yuanqing Xia
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Computational Costs ◽  
Detection Algorithms ◽  
Learning Machines ◽  
The Past ◽  
Real World Datasets ◽  
Learning Machine ◽  
Community Detection Algorithm

Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the k-means clustering techniques, we propose a novel community detection method that surpasses traditional k-means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.

A novel node gravitation-based label propagation algorithm for community detection

2019 ◽  
Vol 30 (06) ◽  
pp. 1950049 ◽  
Author(s):  
Mengjia Shen ◽  
Zhixin Ma
Keyword(s):  
Community Detection ◽  
Prior Information ◽  
Linear Time ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm ◽  
Community Detection Algorithm ◽  
And Function

Community detection in networks is a very important area of research for revealing the structure and function of networks. Label propagation algorithm (LPA) has been widely used to detect communities in networks because it has the advantages of linear time complexity and is unnecessary to get prior information, such as objective function and the number of communities. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the algorithm. In this paper, we propose a novel community detection algorithm, named NGLPA, in which labels are propagated by node gravitation defined by node importance and similarity between nodes. To select the label according to the gravitation between nodes can reduce the randomness of LPA and is consistent with reality. The proposed method is tested on several synthetic and real-world networks with comparative algorithms. The results show that NGLPA can significantly improve the quality of community detection and obtain accurate community structure.

scholarly journals Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Xu ◽  
Jin Qi ◽  
Chunxia Zhou ◽  
Xiaoxuan Hu ◽  
Bianjia Xu ◽  
...  
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Adaptive Learning ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Learning Framework ◽  
The Past ◽  
Mutation Operators ◽  
Community Detection Algorithm ◽  
Self Adaptive

The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA) based on kernelk-means (KKM) and ratio cut (RC) objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.

scholarly journals Community detection in networks: a game-theoretic framework

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yan Chen ◽  
Xuanyu Cao ◽  
K. J. Ray Liu
Keyword(s):  
Community Detection ◽  
Real World ◽  
Expectation Maximization ◽  
Detection Method ◽  
Detection Algorithm ◽  
Generative Models ◽  
Detection Methods ◽  
Game Theoretic ◽  
Community Detection Algorithm ◽  
General Community

AbstractReal-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 and thus gain more insights into the complicated networks. Strategic nodes belonging to different communities interact with each other to decide mutual links in the networks. Hitherto, various community detection methods have been proposed in the literature, yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits, i.e., maximizing their own utility functions when forming a community. Given the proposed game-theoretic generative models for communities, we present a general community detection algorithm based on expectation maximization (EM). Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state of the art.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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