Node Similarity Reveals Community Structure in Complex Networks

2013 ◽  
Vol 5 (3) ◽  
pp. 387-392 ◽  
Author(s):  
Pengli Lu ◽  
Shenglong Zhang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Node Similarity

scholarly journals Detecting community structure in complex networks via node similarity

2010 ◽  
Vol 389 (14) ◽  
pp. 2849-2857 ◽  
Author(s):  
Ying Pan ◽  
De-Hua Li ◽  
Jian-Guo Liu ◽  
Jing-Zhang Liang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Node Similarity

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 Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

scholarly journals Interplay between $$k$$-core and community structure in complex networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Irene Malvestio ◽  
Alessio Cardillo ◽  
Naoki Masuda
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Random Graph ◽  
Shell Structure ◽  
Crucial Role ◽  
Configuration Model ◽  
Graph Models ◽  
Random Graph Models ◽  
Empirical Networks

Abstract The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as $$k$$ k -core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost $$k$$ k -shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the $$k$$ k -core decomposition of many empirical networks cannot be explained by the degree of each node alone, or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the $$k$$ k -core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the $$k$$ k -shell structure of the networks can be accounted for by the community structure. We find that preserving the community structure in the randomisation process is crucial for generating networks whose $$k$$ k -core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost $$k$$ k -shells into a small number of communities.

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