Clustering properties of a generalized critical Euclidean network

Algorithm for Detecting Communities in Complex Networks Based on Hadoop

With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the Hadoop platform (called Community Detection on Hadoop (CDOH)) is proposed in this paper. Based on the basic idea of modularity increment, our algorithm implements parallel merging and accomplishes a fast and accurate detection of the community structure in complex networks. Our extensive experimental results on three real datasets of complex networks demonstrate that the CDOH algorithm can improve the efficiency of the current memory-based community detection algorithms significantly without affecting the accuracy of the community detection.

The Statistical Analysis of Top Hubs in Growing Geographical Networks with Optimal Policy

Many practical networks, such as city networks, road networks and neural networks, usually grow up on basis of topological structures and geographical measures. Big hubs, importance of which have been well known in complex networks, still play crucial roles in growing networks with geographical measures. Therefore, it is very necessary to investigate the underlying mechanisms of statistical features of different top hubs in such networks. Here, we propose a growing network model based on optimal policy in geographical ground. Through the statistics of a great number of geographical networks, we find that the degree and position distributions of top four hubs are diverse between them and closely interrelated with each other, and further gain the relationships between the upper limits of top hubs and the size of networks. Then, the underlying mechanisms are explored. Meanwhile, we are diligent to obtain the corresponding relationships of different spatial distribution areas for different top hubs, and compute their abnormal average degrees at different spatial positions, which show significant differences and imply the advantage of spatial positions and intense competition between top hubs. We hope our results could offer useful inspirations for related practical network studies.

Geographical networks stochastically constructed by a self-similar tiling according to population

In real communication and transportation networks, the geographical positions of nodes are very important for the efficiency and the tolerance of connectivity. Considering spatially inhomogeneous positions of nodes according to a population, we introduce a multiscale quartered (MSQ) network that is stochastically constructed by recursive subdivision of polygonal faces as a self-similar tiling. It has several advantages: the robustness of connectivity, the bounded short path lengths, and the shortest distance routing algorithm in a distributive manner. Furthermore, we show that the MSQ network is more efficient with shorter link lengths and more suitable with lower load for avoiding traffic congestion than other geographical networks which have various topologies ranging from river to scale-free networks. These results will be useful for providing an insight into the future design of ad hoc network infrastructures.

Growing networks under geographical constraints

Inspired by the structure of technological weblike systems, we discuss network evolution mechanisms which give rise to topological properties found in real spatial networks. Thus, we suggest that the peculiar structure of transport and distribution networks is fundamentally determined by two factors. These are the dependence of the spatial interaction range of vertices on the vertex attractiveness (or importance within the network) and on the inhomogeneous distribution of vertices in space. We propose and analyze numerically a simple model based on these generating mechanisms which seems, for instance, to be able to reproduce known structural features of the Internet.

Enhancing robustness and immunization in geographical networks

We find that different geographical structures of networks lead to varied percolation thresholds, although these networks may have similar abstract topological structures. Thus, strategies for enhancing robustness and immunization of a geographical network are proposed. Using the generating function formalism, we obtain an explicit form of the percolation threshold qc for networks containing arbitrary order cycles. For three-cycles, the dependence of qc on the clustering coefficients is ascertained. The analysis substantiates the validity of the strategies with analytical evidence.

Geographical networks evolving with an optimal policy

In this article we propose a growing network model based on an optimal policy involving both topological and geographical measures. In this model, at each time step, a node, having randomly assigned coordinates in a 1x1 square, is added and connected to a previously existing node i, which minimizes the quantity ri2/kialpha, where ri is the geographical distance, ki the degree, and alpha a free parameter. The degree distribution obeys a power-law form when alpha=1, and an exponential form when alpha=0. When alpha is in the interval (0, 1), the network exhibits a stretched exponential distribution. We prove that the average topological distance increases in a logarithmic scale of the network size, indicating the existence of the small-world property. Furthermore, we obtain the geographical edge length distribution, the total geographical length of all edges, and the average geographical distance of the whole network. Interestingly, we found that the total edge length will sharply increase when alpha exceeds the critical value alphac=1, and the average geographical distance has an upper bound independent of the network size. All the results are obtained analytically with some reasonable approximations, which are well verified by simulations.

Phase transitions in an Ising model on a Euclidean network

A one-dimensional network on which there are long-range bonds at lattice distances l>1 with the probability P(l) proportional to l(-delta) has been taken under consideration. We investigate the critical behavior of the Ising model on such a network where spins interact with these extra neighbors apart from their nearest neighbors for 0<or=delta<2. It is observed that there is a finite temperature phase transition in the entire range. For 0<or=delta<1, finite-size scaling behavior of various quantities are consistent with mean-field exponents while for 1<or=delta<or=2, the exponents depend on delta. The results are discussed in the context of earlier observations on the topology of the underlying network.

Geographical effects on cascading breakdowns of scale-free networks

Cascading breakdowns of real networks have resulted in severe accidents in recent years. In this paper, we study the effects of geographical structure on the cascading phenomena of load-carrying scale-free networks. Our essential finding is that when networks are more geographically constrained, i.e., more locally interconnected, they tend to have larger cascading breakdowns. Explanations are provided in terms of the effects of cycles and the distributions of betweenness over degrees.

Evolution of a social network: the role of cultural diversity

We present a simple deterministic and based on local rules model of evolving social network, which leads to a network with the properties of a real social system, e.g., small-world topology and assortative mixing. The state of an individual Si is characterized by the values of Q cultural features, drawn from Gaussian distribution with variance sigma. The other control parameter is sociability Ti, which describes the maximal number of connections of an individual. The state of individuals and connections between them evolve in time. As results from numerical computations, an initial diversity of cultural features in a community has an essential influence on an evolution of social network. It was found that for a critical value of control parameter sigma c(Q) there is a structural transition and a hierarchical network with small-world topology of connections and a high clustering coefficient emerges. The emergence of small-world properties can be related to the creation of subculture groups in a community. The power-law relation between the clustering coefficient of a node and its connectivity C(k) approximately k-beta was observed in the case of a scale-free distribution of sociability Ti and a high enough cultural diversity in a population.

Geographical threshold graphs with small-world and scale-free properties

Many real networks are equipped with short diameters, high clustering, and power-law degree distributions. With preferential attachment and network growth, the model by Barabási and Albert simultaneously reproduces these properties, and geographical versions of growing networks have also been analyzed. However, nongrowing networks with intrinsic vertex weights often explain these features more plausibly, since not all networks are really growing. We propose a geographical nongrowing network model with vertex weights. Edges are assumed to form when a pair of vertices are spatially close and/or have large summed weights. Our model generalizes a variety of models as well as the original nongeographical counterpart, such as the unit disk graph, the Boolean model, and the gravity model, which appear in the contexts of percolation, wire communication, mechanical and solid physics, sociology, economy, and marketing. In appropriate configurations, our model produces small-world networks with power-law degree distributions. We also discuss the relation between geography, power laws in networks, and power laws in general quantities serving as vertex weights.

Self-similar disk packings as model spatial scale-free networks

The network of contacts in space-filling disk packings, such as the Apollonian packing, is examined. These networks provide an interesting example of spatial scale-free networks, where the topology reflects the broad distribution of disk areas. A wide variety of topological and spatial properties of these systems is characterized. Their potential as models for networks of connected minima on energy landscapes is discussed.

Phase transitions in an aging network

We consider a growing network in which an incoming node gets attached to the ith existing node with the probability Pi(i) is proportional to ki(beta)taui(alpha)i , where ki is the degree of the ith node and taui its present age. The phase diagram in the alpha-beta plane is obtained. The network shows scale-free behavior, i.e., the degree distribution Pk approximately k(-gamma) with gamma=3 only along a line in this plane. Small world property, on the other hand, exists over a large region in the phase diagram.

Accelerated growth in outgoing links in evolving networks: deterministic versus stochastic picture

In several real-world networks such as the Internet, World Wide Web, etc., the number of links grow in time in a nonlinear fashion. We consider growing networks in which the number of outgoing links is a nonlinear function of time but new links between older nodes are forbidden. The attachments are made using a preferential attachment scheme. In the deterministic picture, the number of outgoing links m (t) at any time t is taken as N (t)(theta) where N (t) is the number of nodes present at that time. The continuum theory predicts a power-law decay of the degree distribution: P (k) proportional to k-(1-2/ (1-theta ) ), while the degree of the node introduced at time t(i) is given by k(t(i),t)=t(theta)(i) [t/t(i) ]((1+theta)/2) when the network is evolved till time t. Numerical results show a growth in the degree distribution for small k values at any nonzero theta. In the stochastic picture, m (t) is a random variable. As long as <m (t) > is independent of time, the network shows a behavior similar to the Barabási-Albert (BA) model. Different results are obtained when <m (t) > is time dependent, e.g., when m (t) follows a distribution P (m) proportional to m(-lambda). The behavior of P (k) changes significantly as lambda is varied: for lambda>3, the network has a scale-free distribution belonging to the BA class as predicted by the mean field theory; for smaller values of lambda it shows different behavior. Characteristic features of the clustering coefficients in both models have also been discussed.

Scale-free network on a vertical plane

A scale-free network is grown in the Euclidean space with a global directional bias. On a vertical plane, nodes are introduced at unit rate at randomly selected points and a node is allowed to be connected only to the subset of nodes which are below it using the attachment probability, pi(i)(t) approximately k(i)(t)l(alpha). Our numerical results indicate that the directed scale-free network for alpha=0 belongs to a different universality class compared to the isotropic scale-free network. For alpha<alpha(c), the degree distribution is stretched exponential in general which takes a pure exponential form in the limit of alpha-->- infinity. The link length distribution is calculated analytically for all values of alpha.