Controllability and modularity of complex networks

Sun, Peng

doi:10.1016/j.ins.2015.07.024

Cited by 25 publications

(7 citation statements)

References 34 publications

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“…all exist in form of complex networks. e basic research of complex networks is to understand the static structure and dynamic characteristics of networks [1], such as the construction of networks, the topological characteristics of networks, the community structure [2], and synchronization of networks [3,4]. Extensive research on complex networks has enriched our understanding of real-world networks.…”

Section: Introductionmentioning

confidence: 99%

Controllability and Optimization of Complex Networks Based on Bridges

et al. 2020

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In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based on degree of source or target nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths.

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Section: Introductionmentioning

confidence: 99%

Controllability and Optimization of Complex Networks Based on Bridges

et al. 2020

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“…The interactions in these networks play an important role in identifying the networks’ structure, functionality and dynamics. Hence, many researchers utilize the detailed interaction information to do a lot of interesting research works, such as the centrality measures 6 , 7 , community detection 8 , robustness analysis 9 – 11 , controllability 12 and network game 13 – 15 . However, the link information of networks is often invisible in many cases.…”

Section: Introductionmentioning

confidence: 99%

A Robust Method for Inferring Network Structures

Yang

Luo

et al. 2017

Sci Rep

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Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov's smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.Almost everything in our daily life can be modeled as complex networks, such as the social network 1, 2 , transportation network 3 , protein-to-protein network 4 and knowledge graph 5 . The interactions in these networks play an important role in identifying the networks' structure, functionality and dynamics. Hence, many researchers utilize the detailed interaction information to do a lot of interesting research works, such as the centrality measures 6, 7 , community detection 8 , robustness analysis 9-11 , controllability 12 and network game [13][14][15] . However, the link information of networks is often invisible in many cases. Hence, it's very important to propose a robust method in inferring the network structure from very few observed values (measurements) with noise, which are indirectly generated from the network.Many researchers have made great efforts in solving the network inference problem. For instance, Han and Di et al. 16 proposed the state-of-art method i.e., Lasso (Least Absolute Shrinkage and Selection Operator) 17 , which provides an estimation of a network with limited connectivity and low model prediction error. Hempe 18 proposed an inference algorithm based on inner composition alignment to identify the network structure 19, 20 on the time series data 21 . Timme 22 and Domenico Napoletani et al. 23 inferred the complete connectivity of a network from its stable response dynamics. Daniel Marbach et al. 24 summarized many robust gene network inference methods and compared their performances. Soheil Feizi et al. 25 proposed network deconvolution as a general method to distinguish direct dependencies in gene expression regulatory networks.Though many researchers have made great efforts in solving the network inference problem, it is still very challenging mainly on the following reasons. Firstly, the interactions in the network are very sparse. For most of the vertices...

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“…The approaches have been studied in terms of network and diffusion effects, two concepts that we have adapted to our scenario to discuss the rich dynamics produced by the use of random information for social network control. The network effect is analyzed through the changes induced on the network average degree and the clustering coefficient, while the diffusion effect is based on two ad-hoc metrics defined to measure the degree of knowledge diffusion and skill level, and of the polarization of agent interests [48].…”

Section: Goal and Motivationsmentioning

confidence: 99%

Controllability of social networks and the strategic use of random information

Cremonini

Casamassima²

2017

Comput Soc Netw

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BackgroundThis work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception.MethodsOur research is based on a model of knowledge diffusion applied to a time-varying adaptive network and considers two well-known strategies for influencing social contexts: One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad hoc metrics which are defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests.ResultsThe results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills.ConclusionsThese findings support our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable.

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Controllability and modularity of complex networks

Cited by 25 publications

References 34 publications

Controllability and Optimization of Complex Networks Based on Bridges

Controllability and Optimization of Complex Networks Based on Bridges

A Robust Method for Inferring Network Structures

Controllability of social networks and the strategic use of random information

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