# Competition and multiscaling in evolving networks

@article{Barabasi2000CompetitionAM, title={Competition and multiscaling in evolving networks}, author={G. Bianconi A.-L. Barab'asi}, journal={EPL}, year={2000}, volume={54}, pages={436-442} }

The rate at which nodes in a network increase their connectivity depends on their fitness to compete for links. For example, in social networks some individuals acquire more social links than others, or on the www some webpages attract considerably more links than others. We find that this competition for links translates into multiscaling, i.e. a fitness- dependent dynamic exponent, allowing fitter nodes to overcome the more connected but less fit ones. Uncovering this fitter-gets-richer…

## 740 Citations

### Weighted competition scale-free network.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2004

A model of weighted scale-free networks incorporating a fit-gets-richer scheme which means the connectivity of the node depends on both the degree and fitness of the nodes, which indicates that asymptotically the scaling behaviors of the total weight distribution and the connectivity distribution are identical.

### Emergence Of Multiscaling In Heterogeneous Complex Networks

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Numerical evidence of the richer behavior of the connectivity degrees in heterogeneous preferential attachment networks in comparison to their homogeneous counterparts is provided.

### Weighted Fitness Model in Complex Networks

- Computer Science2012 Spring Congress on Engineering and Technology
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Uncovering this fitter-gets-richer phenomenon can help to understand in quantitative terms the evolution of many competitive systems in nature and society and Internet.

### Evolving Scale-Free Local Networks with Fitness and Tunable Clustering

- Computer Science2009 International Conference on Computational Intelligence and Software Engineering
- 2009

The analytical and numerical expressions of the model consistent with the numerical simulations well are indicated, and the model explains the fitter-gets-richer phenomenon in local-world better, and helps us quantificationally comprehend many competitive systems’ evolution in nature and society.

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It is shown that networks with and without weight-topology correlations can emerge from the same simple growth dynamics of the nodes connectivities and of the links weights.

### Scale-free networks from varying vertex intrinsic fitness.

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A new mechanism leading to scale-free networks is proposed, which is called a good-get-richer mechanism, in which sites with larger fitness are more likely to become hubs (i.e., to be highly connected).

### Priority Weighted Fitness Model in Complex Networks

- Computer Science2012 International Conference on Computer Science and Service System
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A model based on these ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena.

### Natural emergence of clusters and bursts in network evolution

- Computer SciencePhysical Review X
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This theoretical model shows that complex network structure can be generated without artificially imposing multiple dynamical mechanisms and may reveal potentially overlooked mechanisms present in complex systems.

### Network dynamics: the world wide web

- Computer Science, Physics
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This thesis finds power-law distributions in website sizes, traffic, and links, and more importantly, develops a stochastic theory which explains them and demonstrates that the Web is a “small world”.

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### Equation (9) can also be derived from the normalization condition 2k0t = jjN(t) kj, a " mass conservation " law, giving the total number of links in the network at time t

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