Why are we interested in networks? Because a enormous categories of phenomena can be modeled using networks. Internet, society, physics, biology, ecology, economy, even the evolution of the universe are essentially networks.
People have found a lot of scaling phenomena in networks.
arXiv:cond-mat/0008465 for the scaling in internet links. What they find is that the distribution of degrees is power-law, i.e., $P\propto k^{-\gamma}$ with $\gamma\sim 2$. arXiv:cond-mat/0009090 provides a better explanation. The explanation is simply popular is more attractive.
In the example of internet links, popular is attractive can be modeled by a dynamic generation of network following some rules.
The probability of attaching to some node with degree $k_i$ is
where $A$ serves as a initial attractiveness since it tells us the probability of attachment when the degree $k_i=0$.
Dorogovtsev et al proved that this model gives us the final distribution of degrees
where $\gamma=2 + A/m$ ^{3}.
The problem about popularity is that we are all aware that popularity is not the only factor.
Consider the following examples.
In some cases similar is also important.
What we would expect is that larger popularity and small similarity (more similar) are the preferable connections. Thus competitions between the two determines the overall connection probability.
To combine the two factors, we use the metric $\mathrm{Popularity}\times \mathrm{Similarity}$. Even though there is a competition between popularity and similarity, small values of $\mathrm{Popularity}\times \mathrm{Similarity}$ are more preferable connections, which takes similarity more seriously.
A simple model to demonstrate this is to build a space of disc. The radius is time $t$, while angles are the measure of similarity. A smaller angler distance indicates a smaller similarity. Using this mapping, we are able to show the dynamics of networks, since we can tell the history of the nodes.
Mathematically speaking, the similarity is measured as
while time is the radius with $t=0$ at the center of the disc.
Now we are going to dynamically generate a network. The updating rules are listed bellow.
The key transformation of this problem is to define a new radial coordinate system $r =\ln t$, so that the distance becomes log scale. Then we define the disc to be on a hyperbolic space so that the distance between any two points is calculated as ^{4}
Minimizing $x_{ij}$ becomes equivalent to minimizing $t_j \theta_{ij}/2$ when dealing with the connectivity from a new born node $i$. The competition between popularity and similarity is simply the minimization of distance between two nodes on a hyperbolic plane.
Why is this definition of distance far superior than the previous measure of $\mathrm{Popularity}\times \mathrm{Similarity}$? Or why do I care? Because the universe/nature itself measures distance on hyperbolic space, i.e., Minkowski space. It also shows us the hint that by choosing a proper metric we might be able to define distance between any nodes in any systems. The only question becomes which geometry.
This result is astonishing also because a discrete system is most likely generated dynamically according to some laws. The method of mapping nodes to a hyperbolic space provides a convenient metric or distance for us to find out the closest nodes. In other words, a node always connect to it’s nearest neighbors if the metric/distance is well defined.
In many real networks, early nodes are more popular. However, popularity decreasing with time can also be modeled. The idea is simple. We allow the nodes to drift away from the center along its own radial direction according to the restraint
where $r_j$ is an old node and it drifts, $r_i$ is the node at current time. Why would we do this? As $\beta$ approaches 1, we fall back to the situation that the nodes are stationary and no drifting is allowed. On the other hand, $\beta$ approaches 0 means we have all the nodes drifting to the circle of current time.
The drifting effectively decrease the connectivity of the old nodes since they are drifting away from the center. Another view of this fading is that the connection probability power law index $\gamma$ is larger, i.e.,
Boguna et al modeled the growth of internet AS using the popularity similarity method and it shows exactly the same statistical result as the actual internet AS data ^{5}.
Krioukov et al came up with the idea that the whole universe can be mapped onto hyperbolic space and events are connected only to nearest neighbors. This in fact is already true in cosmology. Theories predicted that the matter density after inflation is power law, which can be explained by a dynamics generating of particle on a hyperbolic space. The authors created a map from the physical de Sitter space to hyperbolic space. Then the dynamic generation of the particles in the universe simple follows power law since the nodes generated this way has a power law distribution of degrees, i.e., a place that is dense initially is more likely to be dense after the inflation ^{6}.
Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509–512. ↩
Krapivsky, P. L., Redner, S., & Leyvraz, F. (2000). Connectivity of growing random networks. Physical Review Letters, 85(21), 4629–4632. ↩
This express is specific for curvature $K=-4$. However, the final result of scaling doesn’t dependent on the curvature itself. ↩
Boguñá, M., Papadopoulos, F., & Krioukov, D. (2010). Sustaining the Internet with hyperbolic mapping. Nature Communications, 1(6), 1–8. ↩
Krioukov, D., Kitsak, M., Sinkovits, R. S., Rideout, D., Meyer, D., & Boguñá, M. (2012). Network Cosmology. Scientific Reports, 2, 793. ↩