Evolving Network Model Research Articles

Reproduction cost reduces demographic stochasticity and enhances inter-individual compatibility

A population׳s survival depends on its ability to adapt to constraints impinging upon it. As such, adaptation is at the heart of an increasing number of theoretical models. In this paper, we propose a bottom-up evolutionary model to explore the relationship between individual evolutionary dynamics and population-level survival. To do so, we extend a well-established model of gene network evolution by introducing a cost for reproduction. As a result population sizes fluctuate and populations can even go extinct. We find that if a population survives a small and critical number of generations, it will reach a quasi-stationary state which ensures long-term survival. In a constant environment, individual adaptation occurs in response to changes in a populations genetic composition. We show that genetic compatibility increases over generations as a by-product of selection for robustness, thus preventing extinction. We also demonstrate that the number of reproductive opportunities per individual, initial population size, and mutation rates all influence population survival. Finally, mixing different populations reveals that individual properties of gene networks co-evolve with the genetic composition of the population in order to maximize an individuals reproductive success.

Urban Road Network Evolution to Maximize the Capacity

Urban network planning efforts is to achieve the maximum capacity under a certain road investment. As we all know that a good decision can save cost and increase the capacity of the total network. In our previous study, a new computational model of urban spatial evolution has been studied, where people migrate between urban locations, balancing their tendency to explore with their preferential wish to join existing communities. Based on the previous results, this paper presents an urban network evolution model considering road investment and capacity. When a new community is generated, decision-makers will plan to investigate the road construction according to the important degree. Generally speaking, high investment leads to a high road level and big capacity. The decision's objective is to maximum the capacity of the road. In order to achieve this goal, our model focuses on optimal road network, considering maximum capacity under the limited investment, and can determine which road to build and what level of the road. A simulation model is developed to include the topology of road evolution, the structure of road level, and the changes of network capacity over time. Specially, we also simulate the road network evolution with different initialization (with one center and two centers). On one hand, simulation results show that the high investment can bring the big capacity, and different structures can be gain with different initialization. On the other hand, it is also concluded that more centers can improve the traffic capacity of road network.

The Study of Local-world Network Evolution Model Based on Microblog

The Study of Local-world Network Evolution Model Based on Microblog

Estimating Local Decision-Making Behavior in Complex Evolutionary Systems

Research in systems engineering and design is increasingly focused on complex sociotechnical systems whose structures are not directly controlled by the designers, but evolve endogenously as a result of decisions and behaviors of self-directed entities. Examples of such systems include smart electric grids, Internet, smart transportation networks, and open source product development communities. To influence the structure and performance of such systems, it is crucial to understand the local decisions that result in observed system structures. This paper presents three approaches to estimate the local behaviors and preferences in complex evolutionary systems, modeled as networks, from its structure at different time steps. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the local decision-making behaviors from consecutive snapshots of the system structure. In the third approach, the entities are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-centric framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The approaches are illustrated and compared using an example of the autonomous system (AS) level Internet. The approaches are generally applicable to a variety of complex systems that can be modeled as networks. The insights gained are expected to direct researchers in choosing the most applicable estimation approach to get the node-level behaviors in the context of different scenarios.

Constitutive modeling of strain-induced crystallization in filled rubbers.

Strain-induced crystallization is a unique crystallization process taking place solely in polymers subjected to large deformations. It plays a major role for reinforcement and improvement of mechanical properties of polymers with a high regularity of the molecular structure. In this paper, we develop a micromechanical model for the strain-induced crystallization in filled rubbers. Accordingly, the strain-induced crystallization is considered as a process triggered by fully stretched and continued by semistretched polymer chains. The model extends the previously proposed network evolution model [Dargazany and Itskov, Int. J. Solids Struct. 46, 2967 (2009)] and can thus, in addition to the stress upturn and evolution of crystallinity, take into account several inelastic features of filled rubbers, such as the Mullins effect, permanent set, and induced anisotropy. Finally, the accuracy of the model is verified against different set of experimental data both with respect to the stress-strain and crystallization-strain relations. The model exhibits good agreement with the experimental results, which, besides its relative simplicity, makes it a good option for finite-element implementations.

Random clique evolving network model and their communicability

We introduce a notion of random clique evolving network, this network start from a complete subgraph of a -clique, where a is the size of the clique. In every time step T , m nodes are chosen from this network randomly, and forming a new complete subgraph of a -clique. In this way, this network grows in time steps. The numerical investigation shows that the cumulative degree distribution of this network takes an exponential function, which is the property of the homogeneous networks, and the clustering coefficient of this network is larger than that of the ER network. However, the characteristic path length of this network is the similar to that of the ER network, so this network shows the behaviors of small-world networks. Subsequent study shows that this network exhibits hierarchical modular structure for the clustering spectrum vs. k takes power-law. These results are in good agreement with the empirical results on many real-world complex networks, such as urban bus translation network or urban subway network, our model can explain the evolutionary procedure of these spatial networks. Whats more, we present a numerical investigation on the communicability of our model by the Estrada index EE ( G ), the Estrada index EE ( G ) of this network increases with decreasing the rata m / a at the same size N and the same average degree k >. The communicability of the urban public translation networks is very important, our results have a certain guiding significance for the construction of urban bus translation network and urban subway network.

A WEIGHTED EVOLVING NETWORK WITH AGING-NODE-DELETING AND LOCAL REARRANGEMENTS OF WEIGHTS

In previous study of complex network, researchers generally considered the increase of the un-weighted network by the method of adding new nodes and new links. However, most of real networks are weighted and characterized by capacities or strength instead of a binary state (present or absent), and their nodes and links experience both increase and deletion. Barrat, Barthlemy and Vespignani, Phys. Rev. Lett.92, 228701 (2004) presented an evolutionary model (BBV model) to investigate weighted networks. We present a weighted evolution network model based on BBV model, which not only considers to add a new node and m links, but also to remove an old node and corresponding links with probability at each time step. By using rate equation and mean-field method, we study the network's properties: The weight, strength and their distributions. We find that the relationship between weight and strength is nonlinear. In addition, we theoretically prove that the weight distribution and the strength distribution follow a power-law distribution, respectively.

Opinion evolution model of social network based on information entropy

With the development of network services, social networking has become a new medium of information dissemination. Thus, many scholars pay close attention to the evolution of network public opinion which has great significance and practical value. In this paper, an opinion evolution model of social network based on information entropy is proposed, which is helpful to investigate the evolution of network public opinion. The model has two characteristics: firstly, it can reflect the psychological process when an individual makes a choice facing with two opposite views; secondly, it can reflect the influence of subjective and objective factors when the individual forms new ideas. In the simulation, some issues are discussed which includes the effects of the public opinion environment on the individual opinion evolution, the influences of the initial opinion and self-confidence on the individual opinion evolution, and the effects of leaders' opinions on the opinion evolution of populations. From the experimental results it follows that the model works well and can reflect the characteristics of individuals in real social networks. For example, the formation of the individual's opinion will be affected by public opinion environment, and an individual will not accept the opinions from others if he is very confident, and the opinion evolution of populations will be influenced when there exist the leader's opinions in network.

Synchronization in a Novel Local-World Dynamical Network Model

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.

A complex network evolution model for network growth promoted by information transmission

In many real complex networks, information transmission occurs all the time. To study the effects of information transmission on the complex network evolution, we propose a new model for network growth promoted by the information transmission. The model includes three major steps: (i) New links attached to the nodes on the information transmission path, whose source point is chosen preferentially; (ii) the first link of the new node attached to the nodes in the local-world; (iii) other links of the new node attached to the nodes on the information transmission path, whose source point is the new node. The process of information transmission is simulated by self-avoiding random walk, and by considering the local information including its degree and distance; selective connection is established between the nodes on the information transmission path. Theoretical analysis and numerical simulation results show that the proposed model can not only reproduce small-world and scale-free network characteristics, but also indicate that shift power-law distribution and truncated power law function may form for different parameters which have some non-power-law features, such as exponential cutoff, and saturation for small variables. Moreover, in our model, the clustering coefficient is tunable without changing the degree distribution, and the model can also construct a network with assortative or disassortative mixed pattern.

An evolution model of microblog user relationship networks based on complex network theory

Microblog provides convenience to the society, but at the same time, it also brings some adverse effects. To obtain the propagation mechanism of microblog rumor, and then take effective measures to prevent its spread, according to the complex network theory, in this paper we investigate the internal characteristics of microblog user relationship networks, and present a microblog user relationship network evolution model. By using the mean-field theory, the topological statistical property of our evolution model, and the dynamical behaviors of rumor spreading on such a model are analyzed. Theoretical analysis and simulation results show that such an evolving network exhibits a scale-free property. The degree distribution exponent not only is related to the reverse connection probability, but also depends on the node attraction degree distribution. It is also found that when the node attraction degree follows a power-law distribution, the steady-state rumor prevalence is great compared with the exponential distribution and uniform distribution. Moreover, as the reverse connection probability or the number of node initial edges increases, the probability of rumor outbreak and the number of nodes finally infected by the rumor will also increase.

The evolution of Bab paralog expression and abdominal pigmentation among Sophophora fruit fly species

The evolution of gene networks lies at the heart of understanding trait divergence. Intrinsic to development is the dimension of time: a network must be altered during the correct phase of development to generate the appropriate phenotype. One model of developmental network evolution is the origination of dimorphic (male-specific) abdomen pigmentation in the fruit fly subgenus Sophophora. In Drosophila (D.) melanogaster, dimorphic pigmentation is controlled by the dimorphic expression of the paralogous Bab1 and Bab2 transcription factors that repress pigmentation. These expression patterns are thought to have evolved from a monomorphic ancestral state. Here we show that the spatial domain and contrast in dimorphic Bab expression increases during the latter half of pupal development, and this late pupal expression is necessary and sufficient to suppress pigmentation. Late pupal Bab expression was monomorphic for species from basal clades exhibiting monomorphic pigmentation, though dimorphic expression was observed in D. pseudoobscura that represents an intermediate-branching monomorphic clade. Among species from the dimorphic Sophophora clades, Bab expression was dimorphic, but a poor correlation was found between the domains of expression and male pigmentation. Lastly, while Bab paralog co-expression was generally observed, an instance of paralog-specific expression was found, indicating more complex regulatory mechanisms and mutational effects have shaped the evolution of the bab locus. These results highlight the importance of the time and place of Bab expression for pigmentation development and evolution, and suggest that dimorphism evolved early in Sophophora, but diversity in male pigmentation was not further shaped by alterations in Bab expression.

Corbi: a new R package for biological network alignment and querying

In the last decade, plenty of biological networks are built from the large scale experimental data produced by the rapidly developing high-throughput techniques as well as literature and other sources. But the huge amount of network data have not been fully utilized due to the limited biological network analysis tools. As a basic and essential bioinformatics method, biological network alignment and querying have been applied in many fields such as predicting new protein-protein interactions (PPI). Although many algorithms were published, the network alignment and querying problems are not solved satisfactorily. In this paper, we extended CNetQ, a novel network querying method based on the conditional random fields model, to solve network alignment problem, by adopting an iterative bi-directional mapping strategy. The new method, called CNetA, was compared with other four methods on fifty simulated and three real PPI network alignment instances by using four structural and five biological measures. The computational experiments on the simulated data, which were generated from a biological network evolutionary model to validate the effectiveness of network alignment methods, show that CNetA gets the best accuracy in terms of both nodes and networks. For the real data, larger biological conserved subnetworks and larger connected subnetworks were identified, compared with the structural-dominated methods and the biological-dominated methods, respectively, which suggests that CNetA can better balances the biological and structural similarities. Further, CNetQ and CNetA have been implemented in a new R package Corbi (http://doc.aporc.org/wiki/Corbi), and freely accessible and easy used web services for CNetQ and CNetA have also been constructed based on the R package. The simulated and real datasets used in this paper are available for downloading at http://doc.aporc.org/wiki/CNetA/.

A New Scale Free Evolving Network Model with Community Structure

Understanding and modeling the structure of a complex network can lead to a better knowledge of its evolutionary mechanisms, and to a better cottoning on its dynamic and functional behavior. The nodes within a network not only tend to connect the nodes with high degree (scale-free property), and tend to connect with their relatively close distance nodes (community structure property), and the high-degree nodes are easier to connect with their relatively far nodes comparing with the low-degree nodes in the process of network evolution. This phenomenon has been commonly observed from many real-world networks, ranging from social to biological even to technical networks. To model this kind of networks, the present letter proposes a scale free network model with community structure (SFC) to capture and describe their essential topological properties. Numerical simulations indicate that the generated network based on SFC model has scale-free and community structure property. Under the control of the parameters of the model, the community structure of network can be adjustable.

Development of P-TRANE: GIS-Based Model of Bus Transit Network Evolution

This paper describes the development and the main components of a simulation framework aimed to model the bus transit network evolution. The prediction model of transit network evolution (P-TRANE) is a geographic information system (GIS)-based model that develops future predictions of service changes in the bus transit spatial network and service functionality at future time steps. These changes happen as a result of the periodic service review process and are influenced by a group of variables, including the changes in the future transit ridership pattern and future land use distribution changes. The main objective for P-TRANE is to function as a transit supply prediction component for the transportation/land use modeling simulation frameworks, such as the integrated land use, transportation, and environment (ILUTE), by generating the future bus network (supply) information for ILUTE. However, P-TRANE could be an aid to transit agencies and urban planners by means of providing a decision support system prediction tool. Preliminary model results on the case study of the Toronto Transit Commission bus network show that it is successful in identifying transit routes prone to frequency changes (poor performance and/or overcrowding). Furthermore, results show that the P-TRANE GIS module is a promising tool capable of proposing new bus lines, including feeder lines serving rapid transit projects/stations.

Evolving network model with local-area preference for Mobile Ad Hoc Network

To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model, which is analyzed by the mean field theory, is to optimize network structures based on users' behaviors in MANETs. The analysis results indicate that the network generated by this evolving model is a kind of scale-free network. This evolving model can improve the fault-tolerance performance of networks by balancing the connectivity and two factors, i.e., the remaining energy and the distance to nodes. The simulation results show that the evolving topology model has superior performance in reducing the traffic load and the energy consumption, prolonging network lifetime and improving the scalability of networks. It is an available approach for establishing and analyzing actual MANETs.

A Separable Model for Dynamic Networks

Models of dynamic networks - networks that evolve over time - have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model - a Separable Temporal ERGM (STERGM) - facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model, and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analyzing a longitudinal network of friendship ties within a school.

A last updating evolution model for online social networks

As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.

Study of Evolution Model of China Education and Research Network

By searching the hyperlinks with domain name “.edu.cn” which constitutes the China Education and Research Network, we build a complex directed network containing 366,422 web pages containing 540,755 URLs. These URLs constitute a complex directed network through self-organization. By analyzing the topology of China Education and Research Network, we found that it is different from the common Internet in several aspects. Most of the vertices have incoming links, a few vertices have outgoing links, and very few vertices have both incoming and outgoing links. The vertex distribution has a power-law tail. A large proportion of newly added edges always connect with those pages selected from one subnetwork that they belong to, instead of connecting with the pages selected from the whole network. According to these features, we presented the evolution model of this complex directed network. The results indicate that this model reflects some main characteristics of China Education and Research Network.

SH3 interactome conserves general function over specific form.

Src homology 3 (SH3) domains bind peptides to mediate protein-protein interactions that assemble and regulate dynamic biological processes. We surveyed the repertoire of SH3 binding specificity using peptide phage display in a metazoan, the worm Caenorhabditis elegans, and discovered that it structurally mirrors that of the budding yeast Saccharomyces cerevisiae. We then mapped the worm SH3 interactome using stringent yeast two-hybrid and compared it with the equivalent map for yeast. We found that the worm SH3 interactome resembles the analogous yeast network because it is significantly enriched for proteins with roles in endocytosis. Nevertheless, orthologous SH3 domain-mediated interactions are highly rewired. Our results suggest a model of network evolution where general function of the SH3 domain network is conserved over its specific form.