- 12 mins

# Network models

## The Erdős-Rényi model

The Erdős-Rényi model is based on generating a network with N nodes and distributing M links between them evenly or formalised othewrwise, linking the nodes with probability $$p$$.

It was already discussed here that a two nodes are linked with probability $$p$$ and not linked with probability $$1-p$$ therefore resulting in a Binomial degree distribution and in the $$N \rightarrow \infty$$ limit in a Poisson degree distribution where $$\langle k \rangle = Np$$.

The variance can be calculated as well. It can be tiresome to go through $$\sum_{k = 0}^{N}k^{2} \cdot Binom(k, N)$$ which is:

$\langle k^{2} \rangle ~ = ~ \sum_{k=0}^{N} k^{2}\frac{N!}{(N-k)!k!}p^{k}(1-p)^{N-k}$

For this first see:

$\langle k \rangle ~ = ~ \sum_{k=0}^{N} k\frac{N!}{(N-k)!k!}p^{k}(1-p)^{N-k} = Np$

Reparametrizing the equation for $$\langle k^{2} \rangle$$ :

$\langle k^{2} \rangle ~ = Np(N-1)p + Np$

Thus the variance is:

$Var(k) ~ = ~ \langle k^{2} \rangle - \langle k \rangle ^{2} = Np(1-p)$

In the $$N \rightarrow \infty$$ limit $$p \rightarrow 0$$ while the $$\langle k \rangle$$ is constant so therefore the variance becomes negligeble. A large Erdős-Rényi graph is extremely homogenous with no outliers.

Also, all the nodes have approximately the same clustering coefficient so $$C_{i} \approx \langle C \rangle$$. It can be interpreted as probability of neighbors $$i$$ being connected which corresponds to $$p = \langle C \rangle$$.

Giant component in the Erdős-Rényi model:

• Given that the giant component $$S_{1}$$ has a relative size $$S = \frac{S_{1}}{N}$$ the fraction of the nodes that are not in the giant component is $$u = 1 - S$$.
• Taking one node not in the giant component $$i$$ we can say about its connection to $$j$$ the following:
• not connected to $$i$$ directly with probability $$(1-p)$$
• not in the giant component with prob. $$u\cdot p$$
• Therefore we found that if $$i$$ is not connected to any other point or connected but not in the giant component for $$N-1$$ points with probability:
• $$(1-p + pu)^{N-1}$$ which should correspond to not being in the giant component $$u$$

From this equation we can approximate in the large $$N$$ limit that $$\langle k \rangle = 1$$ is the critical avarage node degree above which a giant component forms.

### Generating function

We have the degree distribution $$p(k)$$ and we assume no degree correlation (we are in a random network), we approach the problem from the state where the network can be assumed sparse and local tree-like.

The generating function:

$G_{X}(z) = \sum_{k=0}^{\infty}z^{k}p_{X}(k)$

Where:

$p_{X}(k) = \frac{1}{k!}\frac{dG_{X}(z)}{dz^{k}} \Big| _{z=0}$

And the m$$th$$ moment is:

$\langle X^{m} \rangle ~ = ~ \langle k^{m} \rangle ~ = ~ \Big[ z\frac{d}{dz} \Big]^{m} G_{X}(z) \Big| _{z = 1}$

For $$n$$ intependent variables $$Z = \sum_{i = 1}^{n}X_{i}$$:

$G_{Z}(x) = \prod_{i=1}^{n}G_{X_{i}}(x)$

$$p(k)$$ is the degree distribution function, $$I(k)$$ is the distribution function of a randomly chosen node being in component of size $$k$$, while $$H(k)$$ is the distribution function of a link being connected to a component of size $$k$$ on one end.

$$H_{m}(k)$$ should denote the probability distribution of randomly chosing $$m$$ links which on one end sum up to $$k$$ in component size.

The idea behind creating these PDFs a component can be separated in such a way.

1. Choosing a node with $$m$$ links the probability of the links to sum up to size $$k-1$$. The node and its neighbors therefore sum up to $$k$$ in size and that distribution is $$I(k)$$:
$I(k) = \sum_{m=0}^{\infty}p(m)H_{m}(k-1)$

Taking the generating function of both sides:

$G_{I}(x) ~ = ~ \sum_{k=0}^{\infty}\sum_{m=0}^{\infty}p(m)H_{m}(k-1)x^{k}$

Where:

$H_{m}(k-1) = \frac{1}{(k-1)!}\frac{d^{k-1}}{dx^{k-1}}G_{H, m}(x) | _{x=0} ~ x^{k}$

Since $$H_{m}(k)$$ was chosen that $$m$$ links to sum to $$k$$ in compoennt size, therefore $$G_{H, m}(x) = [G_{H}(x)]^{m}$$, substituting this into $$G_{I}(x)$$:

$G_{I}(x) = \sum_{m=0}^{\infty}p(m)[G_{H}(x)]^{m}x = xG(G_{H}(x))$

Where $$G$$ is the degree distribution’s generating function. From this we can get the mean component size in a random graph:

$\langle S \rangle = G'_{I}(1) = [xG(G_{H}(x))]'|_{x=1} = G(G_{I}(1)) + G'(1)G'_{H}(1)$

Where $$G'(1) ~ = ~ \langle k \rangle$$ but and for calculating $$G'_{H}(1)$$ we should see:

$H(k) = \sum_{m=0}^{\infty}q(m)H_{m}(k-1)$

Where $$q(k)$$ is the probability distribtuion of a randomly choosen link to be able to proceed to $$k$$ direction at one end.

Taking the generating function on both sides and making the same assumptions as before:

$G_{H}(x) = xG_{q}(G_{H}(x))$

And therefore the derivative can be translated into:

$G'_{H}(1) = \frac{1}{1 - G'_{q}(x)}$

So we find that the critical point should be at $$G'_{q}(1) = 1$$ since:

$\langle S \rangle ~ = ~ 1 + \langle k \rangle G'_{H}(1) ~ = ~ 1 + \frac{1}{1 - G'_{q}(1)}$

We can actually calculate $$q(k)$$ by simply saying that:

$P(~node~with~degree~~\underline{k}~~link~at~one~end) ~ = ~\frac{kN_{k}}{\sum_{k'}k'N_{k'}} ~ = ~ \frac{kp(k)}{\langle k \rangle }$

There are $$N_{k}$$ nodes with $$k$$ degrees therefore there are $$N_{k}\cdot k$$ links with a $$k$$ degree node at one end. $$q(k)$$ denotes the probability of a link to have a node at one end with $$k$$ other connections so basically the probability of having a node with a $$k+1$$ degree node at one end:

$q(k) ~ = ~ \frac{k+1}{\langle k \rangle }p(k+1)$

And the generating function of this is simple:

$G_{q}(x) ~ = ~ \sum_{k=0}^{\infty}x^{k}q(k) ~ = ~ \sum_{k=0}^{\infty}x^{k}\frac{k+1}{\langle k \rangle }p(k+1) ~ = ~ \frac{1}{\langle k \rangle }\sum_{l=1}^{\infty}lp(l)x^{l-1} ~ = ~ \frac{G'(x)}{\langle k \rangle }$

We are not done yet, introducing $$q_{m}(k)$$ as the probability of choosing $$m$$ random links that at one end sum to $$k$$ other connections. Therefore the number of second neighbors can be calculated:

$n_{second}(k) ~ = ~ \sum_{m=0}^{\infty}p(m)q_{m}(x)$

Taking the generating function of both sides:

$G_{second}(x) ~ = ~ \sum_{k=0}^{\infty}\sum_{m=0}^{\infty}p(m)q_{m}(x)x^{k} ~ = ~ ... ~ = ~ G(G_{q}(x))$

Finally:

$\langle n_{second} \rangle ~ = ~ G'_{second}(1) ~ = ~ \langle k \rangle G'_{q}(1) ~ \rightarrow ~ G'_{q}(1) ~ = ~ \frac{\langle n_{second} \rangle }{\langle k \rangle }$

After all we arrive at the concluding formula regarding the giant component size:

$\langle S \rangle ~ = ~ 1 + \frac{\langle k \rangle }{1 - \frac{\langle n_{second} \rangle }{\langle k \rangle }} ~ = ~ 1 + \frac{\langle n_{second} \rangle ^{2}}{\langle k \rangle - \langle n_{second} \rangle }$

Therefore if $$\langle k \rangle ~~ \langle ~~ \langle n_{second} \rangle$$ a giant component form, otherwise the system is critical or smaller clusters form only.

It was shown that:

$\langle n_{second} \rangle ~ = ~ \langle k \rangle G'_{q}(1) ~ = ~ \langle k \rangle \sum_{k=0}^{\infty}kq(k) ~ = ~ ... ~ = ~ \langle k^{2} \rangle - \langle k \rangle$

For scale-free networks $$\langle k^{2} \rangle$$ is always divergent therefore they always contain a giant component!

The Erdős-Rényi model does not compare well to real networks, it does not show scale-free behaviour ( no power law degree distibution ), doesn’t have a large clustering coefficient ( $$\langle C \rangle ~ \rightarrow ~ 0$$ )and only shows the small-world effect ( small $$\langle l \rangle$$ ). It is and important reference system but not more.

## The Watts-Strogatz model

It tries to make small-world and local clustering co-exist in a simple random graph by some slight modifications.

### The model

Start from a regular ring of nodes in which the first $$q$$ neighbors are linked and then rewire each link randomly with probability $$\beta$$.

When $$\beta ~ = ~ 0$$ for large $$N$$:

$\langle l \rangle ~=~ \frac{N}{4q}$

And:

$\langle C \rangle ~=~ C = \frac{q(q-1)\frac{1}{3}}{q(2q-1)} ~=~ \frac{3q-3}{4q-2}$

Also when $$\beta ~=~ 0$$ we get back the totally random Erdős-Rényi model:

$\langle l \rangle ~\propto~ logN ~~and~~ \langle C \rangle ~=~ \frac{2q}{N-1}$

There are a range of $$\beta$$ values when $$\langle l \rangle$$ is relatively low while $$C$$ is still very high, meaning high clustering and small wolrd properties.

Takeaway:
It takes a lot of randomness to ruin the clustering, but a very small amount to overcome locality.


Number of random links in the system: $$\beta q N$$. What happens when $$\beta q N \langle \langle 1$$? Basically there will be not many random links that can change the graph thus $$\langle l \rangle ~\propto~ N$$. In the case when $$\beta q N \rangle \rangle 1$$ then the system becomes random and $$\langle l \rangle ~\propto~ lnN$$. Approximating the transition at $$\beta_{critical} q N = 1$$!

It can be shown that a scaling function where:

$l = N \cdot f(N/N_{critical}) \\f(x) = \Big(const. ~ x \langle \langle 1, ~~~ ln(x)/x ~ x \rangle \rangle 1 \Big)$

From numerical studies $$N_{critical} ~\propto~ \beta^{-\tau}$$, therefore:

$l = N\cdot f(\beta^{\tau}N)$

## The Barabási-Albert model

The problem was still open until the Barabási-Albert model of how to generate scale-free random graphs in a simple way.

Motivated by real networks the network size is not static, the system grows at each time step.

A new node can be connected random or can be connected to high degree nodes with a larger probability. $$~\rightarrow~$$ very logical based on real networks, it is called preferential attachement.

Generating procedure:

Adding one node with m links at a time-step (the initial core should be at least containing m nodes) and choosing node i with probability that is proportional to its degree.


For a large $$t$$ timestep:

• $$N ~\propto~ t$$ and $$M ~\propto~ mt$$
• the probability of coohsing node $$i$$ is:
$P_{i} = \frac{k_{i}}{\sum_{j}k_{j}}$
• with this probability $$m$$ new links could be added to node $$i$$ therefore the change in its degree can be approximated by:
$\Delta k_{i} ~\approx~ mP_{i}\Delta t$

From which the differential equation:

$\frac{\partial k_{i}}{\partial t} ~=~ m\frac{k_{i}}{\sum_{j}k_{j}}$

For large $$t$$ the sum of the degrees is twice the number of links $$\sum_{j}k_{j} ~=~ 2M ~=~= 2mt$$ therefore the differential equation is very simle and can be analitically solved:

$\frac{\partial k_{i}}{\partial t} ~=~ \frac{k_{i}}{2t} ~~~~~ \rightarrow k_{i}(t) ~=~ C\sqrt{t}$

Given that at timestep $$t_{i}$$ the degree of node $$i$$ is $$m$$ the constant can be eliminated as $$C = m\cdot t_{i}^{-\frac{1}{2}}$$.

We want to calculate the degree distibution function and expect it to be scale-free. The probabilit of finding a node with degree at least $$k$$ is:

$P(k) = P(k_{i} \langle k) = P(m t_{i}^{-\frac{1}{2}}\sqrt{t} \langle k) = P\Big(\frac{t}{t_{i}} \langle \Big(\frac{k}{m}\Big)^{2}\Big)$

Where:

$P(k) = P\Big(\frac{t_{i}}{t} \rangle \Big(\frac{m}{k}\Big)^{2}\Big)$

So the relative length of the time steps $$t_{i}/t$$ tells that:

$P(k) ~=~ 1 - \Big(\frac{m}{k}\Big)^{2}$

From the cumulative distibution function we can get the degree-distribution by simply derivating by $$k$$ not considering that the problem is discrete. Therefore:

$p(k) ~=~ 2m^{2}k^{-3}$

Which is a scale-free distibution with $$\gamma ~=~ 3$$.

If links are connected with uniform porbability instead of preferential attachement $$P_{i} = 1/N ~\approx~ 1/t$$:

$\frac{\partial k_{i}}{\partial t} ~=~ mP_{i} = \frac{m}{t} ~~~ \rightarrow ~~ k_{i}(t) = m\cdot ln(t/t_{i}) + m$

Making the same chain of calculations this result in a degree distibution that is not scale free $$p_{k} = (e^{1-k/m})/m$$, therefore preferential attachement is truly necessary.

To calculate the clustering coefficient in the Barabási-Albert model we should ask the quation of what the probability is that node $$i$$ intorduced at $$t_{i}$$ is connected to node $$j$$ introduced at $$t_{j}$$.

$P(i - j) = mP_{i} = m\frac{k_{i}}{2mt} = \frac{k_{i}}{2t} = m\Big(\frac{t_{j}}{t_{i}}\Big)^{1/2}\frac{1}{2t_{j}} = \frac{m}{2}(t_{i}t_{j})^{-1/2}$

What is the expected number of links between a nodes links at the end of the generating process which was introduced at timestep $$t_{l}$$?

$n_{l} ~=~ \frac{1}{2}\sum_{t_i = 1}^{N}\sum_{t_j = 1}^{N}P(l-i)P(l-j)P(i-j) = ... = \frac{m^{3}}{16t_l}(lnN)^{2}$

With words: if $$l$$ is linked to $$j$$ and it is linked to $$i$$ the probability of $$i$$ being linked to $$j$$ is $$P(i-j)$$ and it is true for all the links of $$l$$ and therefore the summation, taking the continous limit and actually integrating in the $$...$$ process one can acquire the result above.

The number of links between the neighbors of $$l$$ is $$\approx ~ \frac{k^{2}_l}{2} = \frac{m^{2}N}{2t_{l}}$$ in $$t = N$$. The clustering coefficient is the number of links between the neighbors of $$l$$ in the paths where $$l$$ is present divided by all the edges.

$C = \frac{m^{3}}{16t_l}(lnN)^{2}\frac{2t_l}{m^{2}N} = \frac{m(lnN)^{2}}{8N}$

We can see that it is not $$l$$ dependent at all. This is decaying slower with $$N$$ than the Erdős-Rényi model, however, in real networks there is no decay at all.

Preferential attachement can be measured by simply observing the system for $$\Delta t$$ amount of time.

$\frac{\Delta k_i}{\Delta t} ~\propto~ P(k_{i})$

Taking the integral of $$P(k)$$ for all degrees in order to reduce noise in real networks.

$\kappa (k) = \int P(k)dk$

If $$\kappa$$ is proportional to $$k$$ then there is no preferential attachement if it is proportional to $$k^{2}$$ then there is.

The problem with the Barabási-Albert model so far is that in real networks $$\gamma$$ is $$\in ]2;3[$$ while here it is $$3$$. This results in oldest nodes having the most connections which is not true for real networks either.

We can introduce a finesse $$a$$, a parameter that makes $$\gamma$$ tunable by modifying preferential attachement’s $$P_{i} \propto k_{i} - a$$ probability.

Going to the same process and taking the large $$N$$ limit:

$P_{i} ~=~ \frac{k_{i} - a}{\sum_{j}(k_{j} - a)} = \frac{k_{i} - a}{2M - Na}$

After all the differential equation results in:

$k_{i}(t) = m\cdot\Big(\frac{t}{t_{i}}\Big)^{\frac{1}{2 - a/m}} ~~\rightarrow ~~ p(k) = 2m^{2}k^{-3 + a/m}$

This can also be done with not an additive but a multiplicative finesse parameter $$\eta_{i}$$ which is drawn from a $$\rho(\eta)$$ distribution.

• scale-free : if $$\rho(\eta) = \delta(\eta - \eta_{0})$$ results in the original Barabási-Albert model
• fit-gets-rich : nodes have different $$\eta$$, $$\beta$$ gets larger with $$\eta$$ and it is scale-free and in the long run the largest hubs are the fittest
• Bose-Einsteinn condensation : winner takes all