Tunable Clustering Coefficient Research Articles

Quality of Service Improvement in Neighborhood Area Networking for AMI With ZigBee-Based Tunable Clustered Scale-Free Topology and RPL Routing

This paper deals with the unique development of tunable clustered scale-free neighbourhood area networking (TSFNAN) using ZigBee for establishment of smart grid communication. The complexity of ZigBee based multihop mesh links between gateway and smart meters (SMs) are reduced by extracting the hop reduction feature of scale free and tunable clustering schemes. Further, the routing protocol for low power and lossy networking (RPL) is also adopted to avoid the requirement of route discovery during data transmission. The different service challenges of neighbourhood area networking (NAN) in advanced metering infrastructure of smart grid can thus be met with improved quality of service. A Markov chain delay model is developed to analyse the latency and packet loss in data transmission. The gateway position and the number of SMs are made scalable by correlating the hop count with the tunable clustering coefficient. The proposed system is simulated for a given area with (i) four clusters each containing 2500 SMs and (ii) one cluster containing 10000 SMs. A prototype model with 20 ZigBee nodes is experimented with different coverage range. The system performance is discussed and compared for all such case studies and an encouraging result suggests its practical feasibility with improved QoS.

Regulating clustering and assortativity affects node centrality in complex networks

The limitations of classical node centralities such as degree, closeness, betweenness and eigenvector are rooted in the network topology structure. For a deeper understanding, we regulate the basic network topology structure clustering and assortative coefficient to study the effect on these four classical node centralities. To observe the structural diversity of the complex network, we firstly construct two types of the growing scale-free networks with tunable clustering coefficient and assortative coefficient respectively, and simulate three types of null models on ten real networks to adjust cluster and assortativity. The results indicate that the impact of varying cluster and assortativity on node centrality in complex networks is obvious. We should pay more attention to the network topology when selecting node centralities as identifying the significance or influence of nodes in complex networks.

Identifying node spreading influence for tunable clustering coefficient networks

Identifying the node spreading influence is of significant for information and innovation diffusion. In this paper, we argue that the spreading process should be taken into account for identifying the node spreading influence and investigate the effect of the network structure, measured by the clustering coefficient, on the performance of spreading dynamics. Firstly, we generate a series of networks with tunable clustering coefficients. Then, taking into account the spreading process, we explore the performances among the Dynamics-sensitive (DS) index and the degree, between, closeness, eigenvector indices. Comparing with the Susceptible–Infective–Removed (SIR) model, the extensive results show that, for different spreading time steps and clustering coefficients, the DS centrality outperforms the performance, τ>0.97, of degree, betweenness, closeness and eigenvector measures. Moreover, the accuracy of closeness and eigenvector centrality is similar and conducts better in networks with larger spreading rate β=0.20,τ>0.93. As the clustering coefficient increases, all the performances decrease but DS centrality with least percent of 1.16 at most under β=0.10, and Closeness with the largest percent of 9.75 under β=0.05. This work suggests that the spreading influence not only depends on the network structure, more importantly, the spreading dynamic process also affect the performance greatly, which should be taken into account simultaneously.

How Clustering Affects Epidemics in Random Networks

Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth process on these graphs that model the spread of new ideas, technologies, viruses, or worms: the diffusion model and the symmetric threshold model. For both models, we characterize conditions under which global cascades are possible and compute their size explicitly, as a function of the degree distribution and the clustering coefficient. Our results are applied to regular or power-law graphs with exponential cutoff and shed new light on the impact of clustering.

How Clustering Affects Epidemics in Random Networks

Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth process on these graphs that model the spread of new ideas, technologies, viruses, or worms: the diffusion model and the symmetric threshold model. For both models, we characterize conditions under which global cascades are possible and compute their size explicitly, as a function of the degree distribution and the clustering coefficient. Our results are applied to regular or power-law graphs with exponential cutoff and shed new light on the impact of clustering.

Building Complex Network Similar to Facebook

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

On weightd scale-free network model with tunable clustering and congesstion

We propose an evolutionary model for weighted network with tunable clustering coefficient according to characteristics of real network. The model gives power-law distributions of degree, weight, and strength, as confirmed in many real network. In particular, the weighted model has a nonlinear correlation between average clustering coefficient and degree, which is in good agreement with flat head real weighted technological network. Moreove, the effect of the weighted network structure on traffic delivery is studied. The packet traffic flow on the weighted scale-free network is investigated based on the local routing strategy using node strength, and the delivering ability of node is controlled by node strength. The simulations show that the traffic dynamics depends strongly on the controlled parameter.

Prisoner's Dilemma Game on Clustered Scale-Free Networks under Different Initial Distributions

The evolutionary prisoner's dilemma game is investigated under different initial distributions for cooperators and defectors on scale-free networks with a tunable clustering coefficient. It is found that, on the one hand, cooperation can be enhanced with the increasing clustering coefficient when only the most connected nodes are occupied by cooperators initially. On the other hand, if cooperators just occupy the lowest-degree nodes at the beginning, then the higher the value of the clustering coefficient, the more unfavorable the environment for cooperators to survive for the increment of temptation to defect. Thereafter, we analytically argue these nontrivial phenomena by calculating the cooperation probability of the nodes with different degrees in the steady state, and obtain the critical values of initial frequency of cooperators below which cooperators would vanish finally for the two initial distributions.

Fractal scale-free networks resistant to disease spread

The conventional wisdom is that scale-free networks are prone to epidemic propagation; inthe paper we demonstrate that, on the contrary, disease spreading is inhibited in fractalscale-free networks. We first propose a novel network model and show that itsimultaneously has the following rich topological properties: scale-free degree distribution,tunable clustering coefficient, ‘large-world’ behavior, and fractal scaling. Existingnetwork models do not display these characteristics. Then, we investigate thesusceptible–infected–removed (SIR) model of the propagation of diseases in ourfractal scale-free networks by mapping it to the bond percolation process. Weestablish the existence of non-zero tunable epidemic thresholds by making useof the renormalization group technique, which implies that power law degreedistribution does not suffice to characterize the epidemic dynamics on top of scale-freenetworks. We argue that the epidemic dynamics are determined by the topologicalproperties, especially the fractality and its accompanying ‘large-world’ behavior.

A growing network model with high hub connectivity and tunable clustering coefficient

The paper investigates a model for social network based on a growing network. The model we propose can fit the following two basic properties: (1) 'scale-free property' which has power law degree d...

Synchronization in Triangled ComplexNetworks

Using a tunable clustering coefficient model without changingthe degree distribution, we investigate the effect of clusteringcoefficient on synchronization of networks with both unweightedand weighted couplings. For several typical categories of complexnetworks, the more triangles are in the networks, the worse thesynchronizability of the networks is.

A weighted scale-free network model with large-scale tunable clustering

Based on the weighted scale-free network model proposed by Barrat, Barthélemy and Vespignani (BBV), we propose a generalized BBV (GBBV) model with large-scale tunable clustering coefficient. Theoretical analysis and numerical simulations show that the GBBV model retains many properties of the BBV model, such as power-law distributions of node degree, node strength and edge weight. However, the GBBV model overcomes the drawback of the BBV model that the clustering coefficient can only be tuned in a small interval, and therefore the GBBV model can be used for modeling networks with large clustering coefficients.

Properties of highly clustered networks.

We propose and solve exactly a model of a network that has both a tunable degree distribution and a tunable clustering coefficient. Among other things, our results indicate that increased clustering leads to a decrease in the size of the giant component of the network. We also study susceptible/infective/recovered type epidemic processes within the model and find that clustering decreases the size of epidemics, but also decreases the epidemic threshold, making it easier for diseases to spread. In addition, clustering causes epidemics to saturate sooner, meaning that they infect a near-maximal fraction of the network for quite low transmission rates.