Scale-Free Network Topologies with Clustering Similar to Online Social Networks

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference R and coupling coefficient ε will increase the risks of cascading failures. Cascading threshold values Rc and εc exist, where cascading failures will spread to the entire network when R>Rc or ε>εc. When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree k can improve the network invulnerability.

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Shortest path models for scale-free network topologies: literature review and cross comparisons

Modeling and Simulation of Complex Communication Networks ◽

10.1049/pbpc018e_ch6 ◽

2019 ◽

pp. 145-172

Keyword(s):

Literature Review ◽

Shortest Path ◽

Scale Free Network ◽

Scale Free ◽

Network Topologies ◽

Free Network ◽

Path Models

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The epidemic network construction and immunization based on node strength

Modern Physics Letters B ◽

10.1142/s0217984918503190 ◽

2018 ◽

Vol 32 (26) ◽

pp. 1850319 ◽

Cited By ~ 3

Author(s):

Fuzhong Nian ◽

Longjing Wang ◽

Zhongkai Dang

Keyword(s):

Social Networks ◽

Real World ◽

Scale Free Network ◽

Network Construction ◽

The Real ◽

Scale Free ◽

Social Ability ◽

The Social ◽

Free Network

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization.

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Small World and Scale-Free Network Topologies in an Artificial Regulatory Network Model

Artificial Life IX ◽

10.7551/mitpress/1429.003.0068 ◽

2004 ◽

Keyword(s):

Network Model ◽

Regulatory Network ◽

Small World ◽

Scale Free Network ◽

Scale Free ◽

Network Topologies ◽

Free Network

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Quantitative Controllability Index of Complex Networks

Advances in Mathematical Physics ◽

10.1155/2018/2586536 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Lifu Wang ◽

Yali Zhang ◽

Jingxiao Han ◽

Zhi Kong

Keyword(s):

Complex Networks ◽

Condition Number ◽

Random Network ◽

Small World ◽

Scale Free Network ◽

Scale Free ◽

Network Topologies ◽

Free Network ◽

The Relationship ◽

Controllability Index

In this paper, the controllability issue of complex network is discussed. A new quantitative index using knowledge of control centrality and condition number is constructed to measure the controllability of given networks. For complex networks with different controllable subspace dimensions, their controllability is mainly determined by the control centrality factor. For the complex networks that have the equal controllable subspace dimension, their different controllability is mostly determined by the condition number of subnetworks’ controllability matrix. Then the effect of this index is analyzed based on simulations on various types of network topologies, such as ER random network, WS small-world network, and BA scale-free network. The results show that the presented index could reflect the holistic controllability of complex networks. Such an endeavour could help us better understand the relationship between controllability and network topology.

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Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory

International Journal of Natural Computing Research ◽

10.4018/ijncr.2015010102 ◽

2015 ◽

Vol 5 (1) ◽

pp. 26-41 ◽

Cited By ~ 4

Author(s):

Jean Paul Barddal ◽

Heitor Murilo Gomes ◽

Fabrício Enembreck

Keyword(s):

Social Networks ◽

Data Streams ◽

Concept Drift ◽

Synthetic Data ◽

Scale Free Network ◽

Scale Free ◽

Detection Algorithms ◽

Adaptive Window ◽

Free Network ◽

Mining Data Streams

Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classification task but usually maintain a fixed size ensemble of learners running the risk of needlessly spending processing time and memory. In this paper the authors present improvements to the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for regression that employs social networks theory. In order to detect concept drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show improvements in accuracy, especially in concept drift situations and better performance compared to other state-of-the-art algorithms in both real and synthetic data.

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Evolution of Conformity Dynamics in Complex Social Networks

Symmetry ◽

10.3390/sym11030299 ◽

2019 ◽

Vol 11 (3) ◽

pp. 299 ◽

Cited By ~ 2

Author(s):

Yuhui Gong ◽

Qian Yu

Keyword(s):

Social Networks ◽

Random Network ◽

Small World ◽

Network Size ◽

Small World Network ◽

Scale Free Network ◽

Small World Networks ◽

Dominant Group ◽

Scale Free ◽

Free Network

Conformity is a common phenomenon among people in social networks. In this paper, we focus on customers’ conformity behaviors in a symmetry market where customers are located in a social network. We establish a conformity model and analyze it in ring network, random network, small-world network, and scale-free network. Our simulations shown that topology structure, network size, and initial market share have significant effects on the evolution of customers’ conformity behaviors. The market will likely converge to a monopoly state in small-world networks but will form a duopoly market in scale networks. As the size of the network increases, there is a greater possibility of forming a dominant group of preferences in small-world network, and the market will converge to the monopoly of the product which has the initial selector in the market. Also, network density will become gradually significant in small-world networks.

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