In this section, we study two simple constructions that involve adding points to a given discrete graph.
Our first construction adds endpoints to a given graph and leads to the anlysis of the interesting collection of binomial trees.
Suppose that \((S, \rta)\) is a discrete, graph, so a graph in the combinatorial sense. Let Let \(T = S \cup S^\prime\) where \(S^\prime = \{t_x: x \in S\}\) is a distinct set of elements, disjoint from \(S\) and indexed by \(x \in S\). Extend the relation \(\rta\) to \(T\) by \[ x \rta t_x, \; t_x \rta x, \quad x \in S \] The graph \((T, \rta)\) is the graph obtained from \((S, \rta)\) by endpoint addition.
That is, we simply add an endpoint \(t_x\) to \(x \in S\) by an undirected edge for each \(x \in S\). If the original graph \((S, \rta)\) is symmetric (undirected) then so is \((T, \rta)\). A graph obtained by endpoint addition is always stochastic. The following result shows how to recover the density function from the reliability function.
Suppose that \((S, \rta)\) is a discrete graph and \((T, \rta)\) the graph obtained from \((S, \rta)\) by endpoint addition, as in definition . If \(f\) is a probability density function on \(T\) and \(F\) the corresponding reliability function for \((T, \rta)\) then for \(x \in S\), \begin{align*} f(x) & = F(t_x) \\ f(t_x) & = F(x) - \sum\left\{F(t_y): y \in S, \, x \rta y\right\} \end{align*}
The first part of the displayed equation follows from the construction since \(t_x \rta x\) and \(x\) is the only such point. For the second part, \[F(x) = \sum \{f(y): y \in T, \, x \rta y\} = \sum \{f(y): y \in S, \, x \rta y\} + f(t_x) = \sum\{F(t_y): y \in S, \, x \rta y\} + f(t_x) \]
Given a probability distribution on \(S\), there is a natural way to construct a probability distribution on \(T\).
Suppose that \(f\) is a probability density function on \(S\) and that \(p \in (0, 1)\). Define the probability density function \(g\) on \(T\) by \[ g(x) = p f(x), \; g(t_x) = (1 - p) f(x), \quad x \in S \]
So if \(Y\) is a random variable in \(T\) with density function \(g\) then \(\P(Y \in S) = p\) and \(f\) is the conditional density of \(Y\) given \(Y \in S\). Moreover, \(\P(Y \in S^\prime) = 1 - p\) and the conditional density of \(Y\) given \(Y \in S^\prime\) is \(t_x \mapsto f(x)\).
in the context of definition , let \(F\) denote the reliability function of \(f\) for \((S, \rta)\) and \(G\) the reliability function of \(g\) for \((T, \rta)\). Then \[G(x) = p F(x) + (1 - p) f(x), \; G(t_x) = p f(x), \quad x \in S \]
Let \(x \in S\). Then by construction, \(G(t_x) = g(x) = p f(x)\) since \(t_x \rta x\) and \(x\) is the only such point. On the other hand, \[G(x) = \sum \{g(y): y \in T, \, x \rta y\} = \sum \{g(y): y \in S, \, x \rta y\} + g(t_x) = \sum \{p f(y): y \in S, \, x \rta y\} + (1 - p) f(x) = p F(x) + (1 - p) f(x)\]
Our main result is that if the density function \(f\) has constant rate for \((S, \rta)\) then with an appropriate choice of \(p\), the density function \(g\) has constant rate for \((T, \rta)\).
Suppose that \(f\) is a probability density function on \(S\) that has constant rate \(\alpha \in (0, \infty)\) for \((S, \rta)\). Let \begin{align*} \beta &= \frac{\sqrt{4 \alpha^2 + 1} - 1}{2 \alpha} \\ p &= \frac{1}{1 + \beta} = \frac{2 \alpha}{\sqrt{4 \alpha^2 + 1} + (2 \alpha - 1)} \end{align*} Then the density \(g\) constructed in from \(f\) and \(p\) has constant rate \(\beta\) for \((T, \rta)\).
Let \(x \in S\). Using and the fact that \(f\) has constant rate \(\alpha\) for \((S, \rta)\) we have \[g(x) = \frac{1}{1 + \beta} f(x) = \frac{\alpha}{1 + \beta} F(x) = \alpha \left[G(x) - \frac{\beta}{1 + \beta} f(x) \right] = \alpha [G(x) - \beta g(x)]\] Hence \[g(x) = \frac{\alpha}{1 + \alpha \beta} G(x) = \beta G(x)\] The last equality holds since \(\beta\) is defined by the quadratic equation \(\alpha \beta^2 + \beta - \alpha = 0\). Next, \[g(t_x) = \frac{\beta}{1 + \beta} f(x) = \beta g(x) = \beta G(t_x), \quad x \in S\]
These results follow from calculus.
We can recursively add endpoints to a given graph.
Suppose that \((S_1, \rta)\) is a discrete graph that has a distribution with constant rate \(\alpha_1 \in (0, \infty)\). Recursively, for \(n \in \N_+\), let \((S_{n + 1}, \rta)\) denote the graph obtained from \((S_n, \rta)\) by endpoint addition as in . So this graph has a distribution with constant rate \[ \alpha_{n + 1} = \frac{\sqrt{4 \alpha_n^2 + 1} - 1}{2 \alpha_n}, \quad n \in \N_+ \]
In the context of , \(\alpha_n \to 0\) as \(n \to \infty\).
By construction, \(\alpha_{n + 1} = \varphi(\alpha_n)\) for \(n \in \N_+\) where \[ \varphi(t) = \frac{\sqrt{4 t^2 + 1} - 1}{2 t}, \quad t \in (0, \infty)\] We define \(\varphi(0) = 0\) so that \(\varphi\) is continuous on \([0, \infty)\). So then \(\alpha_n\) is decreasing in \(n\) and converges to the only fixed point of \(\varphi\), namely 0.
The most important application of endpoint addition is to binomial trees, since these trees are generated precisely by this method.
The (undirected) binomial trees are defined recursively as follows:
The binomial tree of order \(n \in \N_+\) has \(2^n\) vertices.
So the vertices of the binomial tree of order \(n \in \N_+\) can be labeled with elements of \(\{0, 1\}^n\), that is, bit strings of length \(n\). It helps to have a consistent way to do that.
The vertices of the binomial trees are labeled with bit strings as follows:
With this labeling, the graph \(B_n = \left(\{0, 1\}^n, \sim\right)\) is the binomial tree of order \(n\). The graph relations are given recursively as follows: \(0 \sim 1\) in \(B_1\) and for \(y, \, z \in \{0, 1\}^n\), \begin{align*} y 0 & \sim y 1 \text{ in } B_{n + 1} \\ y 1 & \sim z 1 \text{ in } B_{n + 1} \text{ if and only if } y \sim z \text{ in } B_n \end{align*}
The following result identifies the degrees of the vertices in the binomial tree. To state the result succinctly, let \(1^{(j)}\) denote the string with 1 repeated \(j\) times \(j \in \N_+\). Let \(1^{(0)}\) denote the empty string.
Let \(B_n\) denote the binomial tree of order \(n \in \N_+\), as defined in .
In particulr, the binomial tree of order \(n \ge 2\) has \(2^{n - 1}\) endpoints. The following result restates in the notation of our bit string labeling:
Suppose that \(n \in \N_+\) and that \(f\) is a probability density function on \(\{0, 1\}^{n + 1}\) that has reliability function \(F\) for the binomial tree \(B_{n + 1}\). Then for \(y \in \{0, 1\}^n\), \begin{align*} f(y 1) & = F(y 0) \\ f(y 0) & = F(y 1) - \sum \{F(z 0): z \sim y \text{ in } B_n\} \end{align*}
With the bit string notation, it's easy to describe the probability distributions on the binomial trees generated recursively by the construction in . A bit of additional notation will help. For \(p \in (0, 1)\), let \(p(0) = 1 - p\) and \(p(1) = p\).
Let \(X_0\) have the point mass distribution on the single vertex in the binomial tree of order 0. For \(n \in \N\), let \(X_{n + 1}\) have the distribution on \(\{0, 1\}^{n + 1}\) constructed from the distribution of \(X_n\) on \(\{0, 1\}^n\) as in , with parameter \(p_{n + 1} \in (0, 1)\). Then the random bits \(I_1 I_2 \cdots I_n\) of \(X_n\) are independent Bernoulli variables and \(I_k\) has parameter \(p_k\) for \(k \in \{1, 2, \ldots, n\}\). That is, the probability density function \(f_n\) of \(X_n\) is given by \[f_n(i_1 i_2 \cdots i_n) = p_1(i_1) p_2(i_2) \cdots p_n(i_n), \quad i_1 i_2 \cdots i_n \in \{0, 1\}^n \]
If \(p_k = p \in (0, 1)\) for \(k \in \N_+\) then the bits of \(X_n\) are a sequence of \(n\) Bernoulli trials with parameter \(p\) for \(n \in \N_+\). The following result gives the distribution of the degree of \(X_n\).
For \(n \in \N_+\) let \(X_n\) denote the random variable in \(\{0, 1\}^n\) constructed in and let \(V_n\) denote the degree of \(X_n\) in the binomial tree \(B_n\). Then \(V_n\) has density function \(h_n\) given by \begin{align*} h_n(k) &= (1 - p_{n - k + 1}) \prod_{j = n - k + 2}^n p_j, \quad k \in \{1, 2, \ldots, n - 1\} \\ h_n(n) &=\prod_{j = 2}^{n} p_j \end{align*}
When \(p_k = p\) for all \(k \in \N_+\) then \(V_n\) has a truncated geometric distribution:
Suppose that \(p_k = p\) for \(k \in \N_+\) and for \(n \in \N_+\) and let \(V_n = \deg(X_n)\) as in .
Of course for us, the most interesting case is when \(X_n\) has constant rate for the binomial tree \(B_n\) for each \(n \in \N_+\). The following results are a special case of .
Let \(\alpha_1 = 1\) and then recursively let \[ \alpha_{n + 1} = \frac{\sqrt{4 \alpha_n^2 + 1} - 1}{2 \alpha_n}, \quad n \in \N_+ \] Let \(p_n = 1 / (1 + \alpha_n)\) for \(n \in \N_+\). Then random variable \(X_n\) in has constant rate \(\alpha_n\) on the binomial tree \(B_n\) for each \(n \in \N_+\).
We can also give a recurrence relation for the parameters \((p_n: n \in \N_+)\) directly: \(p_1 = \frac{1}{2}\) and \[ p_{n + 1} = \frac{2 - 2 p_n}{(2 - 3 p_n) + \sqrt{5 p_n^2 - 8 p_n + 4}}, \quad n \in \N_+ \]
\(\alpha_n \downarrow 0\) and \(p_n \uparrow 1\) as \(n \to \infty\).
Explicit formulas for \(\alpha_n\) and \(p_n\) as a function of \(n \in \N_+\) seem difficult. But we can give the results for the first few values. We already know what happens when \(n = 1\); only the notation is different.
The binomial tree of order 1 is a path on 2 vertices. The rate constant and probability are \(\alpha_1 = 1\) and \(p_1 = 1 / 2\). The constant rate distribution is uniform, with density \(f_1\) given by \(f_1(0) = 1 / 2 = f_1(1) = 1 / 2\).
When \(n = 2\) we also have a path so the result is a special case of general results on paths in Section 7.3.
The binomial tree of order 2 is a path on 4 vertices. The rate constant and probability are \(\alpha_2 = p_2 = \left(\sqrt{5} - 1\right) \big/ 2 \approx 0.6180\). The constant rate distribution has density \(f_2\) given by \begin{align*} f_2(00) & = f_2(10) = \frac{3 - \sqrt{5}}{4} \\ f_2(01) & = f_2(11) = = \frac{\sqrt{5} - 1}{4} \end{align*}
The case \(n = 3\) is different and not treated elsewhere in this text.
For the binomial tree of order 3, the rate constant and probability are \[ \alpha_3 = \frac{\sqrt{7 - 2 \sqrt{5}} - 1}{\sqrt{5} - 1} \approx 0.4773, \; p_3 = \frac{\sqrt{5} - 1}{\sqrt{7 - 2 \sqrt{5}} + \sqrt{5} - 2} \approx 0.6769 \] The constant rate distribution has density \(f_3\) given by \begin{align*} f_3(000) & = f_3(100) = \frac{\left(\sqrt{5} - 3\right) \left(\sqrt{7 - 2 \sqrt{5}} - 1\right)}{4 \left({\sqrt{7 - 2 \sqrt{5}} + \sqrt{5} - 2}\right)} \approx 0.06170\\ f_3(010) & = f_3(110) = \frac{\sqrt{5} - 1}{4} \left(1 - \frac{\sqrt{5} - 1}{\sqrt{7 - 2 \sqrt{5}} + \sqrt{5} - 2}\right) \approx 0.09983\\ f_3(001) & = f_3(101) = \frac{\sqrt{5} - 2}{{\sqrt{7 - 2 \sqrt{5}} + \sqrt{5} - 2}} \approx 0.1293 \\ f_3(011) & = f_3(111) = \frac{\left(\sqrt{5} - 1\right)^2}{4 \left({\sqrt{7 - 2 \sqrt{5}} + \sqrt{5} - 2}\right)} \approx 0.20918 \end{align*}
Of course, the result in on \(V_n = \deg(X_n)\) applies to the constant rate distributions. Rooted, directed binomial trees will be studied in Chapter 5.
Another tractible case arises from endpoint addition to a regular graph. So suppose that \((S, \rta)\) is a right regular graph of degree \(k\) with \(n\) vertices where \(n, \, k \in \N_+\) and \(k \le n\). Let \((T, \rta)\) denote the graph obtained from \((S, \rta)\) by endpoint addition.
The graph \((T, \rta)\) has a constant rate distribution with rate \[ \beta = \frac{\sqrt{k^2 + 4} - k}{2} \] and density function \(g\) given by \begin{align*} g(x) &= \frac{1}{n} \frac{2}{\sqrt{k^2 + 4} + 2 - k}, \quad x \in S \\ g(t_x) & = \frac{1}{n} \frac{\sqrt{k^2 + 4} - k}{\sqrt{k^2 + 4} + 2 - k}, \quad x \in S \end{align*}
Note that the rate constant in depends only on \(k\) (but again we must have \(k \le n\)).
Suppose that random variable \(X\) in \(T\) has the distribution in . Then \begin{align*} \P(X \in S) &= \frac{2}{\sqrt{k^2 + 4} + 2 - k} \\ \P(X \in S^\prime) & = \frac{\sqrt{k^2 + 4} - k}{\sqrt{k^2 + 4} + 2 - k} \end{align*} So \(\beta \to 0\), \(\P(X \in S) \to 1\) and \(\P(X \in S^\prime) \to 0\) as \(k \to \infty\).
Let \(v_m\) denote the right walk function of order \(m \in \N_+\) for the graph \((T, \rta)\). Then \(v_m(x) = a_m\) and \(v_m(t_x) = a_{m - 1}\) for \(x \in S\) where \[a_m = \left(\frac{k + 2 - \sqrt{k^2 + 4}}{2 \sqrt{k^2 + 4}}\right) \left(\frac{k + \sqrt{k^2 + 4}}{2}\right)^m + \left(\frac{3 \sqrt{k^2 + 4} - k - 2}{2 \sqrt{k^2 + 4}}\right) \left(\frac{k - \sqrt{k^2 + 4}}{2}\right)^m\]
Since \((S, \rta)\) is right regular, \(v_m\) is constant on \(S\) and on \(S^\prime\). So let \(a_m\) denote the constant value on \(S\) and \(b_m\) the constant value on \(S^\prime\). Then \begin{align*} a_{m + 1} & = k a_m + b_m \\ b_{m + 1} & = a_m \end{align*} which leads to the second order difference equation \(a_{m + 2} - k a_{m + 1} - a_m = 0\). The corresponding characteristic equation is \(r^2 - k r - 1 = 0\) which has roots \[\frac{k \pm \sqrt{k^2 + 4}}{2}\] Applying the initial conditions \(a_0 = 1\), \(a_1 = k + 1\) then leads to the result.
It follows that the number of walks of length \(m \in \N_+\) is \(w_m = n (a_m + a_{m - 1})\), and hence the right walk distribution of order \(m\) has density function \(g_m\) given by \begin{align*} g_m(x) & = \frac{a_m}{n (a_m + a_{m - 1})}, \quad x \in S \\ g_m(t_x) & = \frac{a_{m - 1}}{n (a_m + a_{m - 1})}, \quad x \in S \end{align*} It's easy to show directly that \(w_m / w_{m + 1} \to \beta\) as \(m \to \infty\) and that \(g_m \to g\) as \(m \to \infty\) where \(\beta\) is the rate constant and \(g\) the constant rate density function in . Of course, these results are guaranteed by the general theory in Section 5. Here are a couple of examples:
Suppose that \((S, \rta)\) is the directed cycle on \(n \in \{3, 4, \ldots\}\) vertices. The constant rate distribution for the graph \((T, \rta)\) obtained by endpoint addition rate \(\beta = (\sqrt{5} - 1) / 2\) and density function \(g\) given by \[g(x) = \frac{\sqrt{5} - 1}{2 n}, \; g(t_x) = \frac{3 - \sqrt{5}}{ 2 n}, \quad x \in S \]
Suppose that \((S, \lfrta)\) is the undirected cycle on \(n \in \{3, 4, \ldots\}\) vertices. The constant rate distribution for the graph \((T, \rta)\) obtained by endpoint addition has rate \(\beta = \sqrt{2} - 1\) and density function \(g\) given by \[g(x) = \frac{1}{\sqrt{2} n}, \; g(t_x) = \left(1 - \frac{1}{\sqrt{2}}\right) \frac{1}{n}, \quad x \in S \]
Our second construction adds a univeral point to a discrete graph.
Suppose that \((S, \rta)\) is a discrete graph and that \(t \notin S\). Extend \(\rta\) to \(S \cup \{t\}\) by making \(t\) a universal point so that \(x \rta t\) and \(t \rta x\) for all \(x \in S\). The graph \((S \cup \{t\}, \rta)\) is the cone graph associated with \((S, \rta)\) and \(t\).
This use of the term cone graph is not the only one, but is convenient for our purposes. It's easy to extend a distribution on \(S\) to one on \(S \cup \{v\}\).
Suppose that \(S\) is a countable set and \(t \notin S\). If \(f\) is a probability density function on \(S\) and \(p \in (0, 1)\), let \(g\) be the probability density function on \(S \cup \{t\}\) defined by \[g(x) = p f(x), \; x \in S; \quad g(t) = 1 - p\]
This is similar to the construction used for endpoint addition in : If \(Y\) is a random variable in \(S \cup \{v\}\) with density \(g\) then the conditional distribution of \(Y\) given \(Y \in S\) has density \(f\), and \(\P(Y \in S) = p\) (so that \(\P(Y = v) = 1 - p\)).
In the context of Definition , suppose that \(F\) is the reliability function of \(f\) for the graph \((S, \rta)\) and \(G\) the reliability function of \(g\) for the cone graph \((S \cup \{t\}, \rta)\). Then \[G(x) = p F(x) + 1 - p, \; x \in S; \quad G(t) = p\]
As usual, we are particularly interested in distributions with constant rate for the cone graph.
Suppose again that \(f\) is a probability density function on \(S\) and that \(F\) is the reliability function of \(f\) for the graph \((S, \rta)\). If \(f = \alpha F + \alpha^2\) for some \(\alpha \in (0, \infty)\) then the density \(g\) constructed in definition with \(p = 1 / (1 + \alpha)\) has constant rate \(\alpha\) for the cone graph \((S \cup \{t\}, \rta)\).
Suppose that \(f = \alpha F + \alpha^2\) for some \(\alpha \in (0, \infty)\) and that \(g\) is constructed as in definition with \(p = 1 / (1 + \alpha)\). Then \begin{align*} g(x) & = p f(x) = \frac{1}{1 + \alpha} f(x) = \frac{\alpha}{1 + \alpha} F(x) + \frac{\alpha^2}{1 + \alpha}, \quad x \in S \\ g(t) & = 1 - p = \frac{\alpha}{1 + \alpha} \end{align*} and \begin{align*} G(x) & = p F(x) + 1 - p = \frac{1}{1 + \alpha} F(x) + \frac{\alpha}{1 + \alpha}, \quad x \in S \\ G(t) & = p = \frac{1}{1 + \alpha} \end{align*} Hence \(g = \alpha G\).
In general, is not very helpful. But if the original graph is right regular, it's easy to construct a constant rate distribution for the corresponding cone graph. So in this subsection, suppose that \((S, \rta)\) is a right regular graph of degree \(k\) with \(n\) points, where \(n, \, k \in \N_+\) with \(k \le n\).
Then the cone graph \((S \cup \{t\}, \rta)\) has a constant rate distribution with rate \[\alpha = \frac{\sqrt{4 n + k^2} - k}{2 n}\] and density funtion \(g\) given by \begin{align*} g(x) & = \frac{2}{2 n - k + \sqrt{4 n + k^2}}, \quad x \in S \\ g(t) & \frac{\sqrt{4 n + k^2} - k}{2 n - k + \sqrt{4 n + k^2}} \end{align*}
Let \(f\) be the uniform density on \(S\) so that \(f(x) = 1 / n\) for \(x \in S\). Since the graph is right regular with degree \(k\) we have \(F(x) = k / n\) for \(x \in S\). From we have \[\frac{1}{n} = \alpha \frac{k}{n} + \alpha^2\] solving gives \[\alpha = \frac{\sqrt{4 n + k^2} - k}{2 n}\] and then \[ p = \frac{1}{1 + \alpha} = \frac{2 n} {2 n - k + \sqrt{4 n + k^2}}\] So then the distribution with density \(g\) constructed in from \(f\) and \(p\) has constant rate \(\alpha\)
Let \(v_m\) denote the right walk function of order \(m \in \N_+\) for the cone graph \((S \cup \{t\}, \rta)\). Then \(v_m(x) = a_m\) for \(x \in S\) and \(v_m(t_) = n a_{m - 1}\) where \[a_m = \left(\frac{3 k + 2}{2 \sqrt{k^2 + 4 n}} - \frac{1}{2}\right) \left(\frac{k + \sqrt{k^2 + 4 n}}{2}\right)^m \left(\frac{3}{2} - \frac{3 k + 2}{2 \sqrt{k^2 + 4 n}}\right) \left(\frac{k - \sqrt{k^2 + 4 n}}{2}\right)^m\]
Since \((S, \rta)\) is right regular, \(v_m\) is constant on \(S\). So let \(a_m\) denote the constant value on \(S\) and let \(b_m = v_m(t)\). Then \begin{align*} a_{m + 1} & = k a_m + b_m \\ b_{m + 1} & = n a_m \end{align*} which leads to the second order difference equation \(a_{m + 2} - k a_{m + 1} - n a_m = 0\). The corresponding characteristic equation is \(r^2 - k r - n = 0\) which has roots \[\frac{k \pm \sqrt{k^2 + 4 n}}{2}\] Applying the initial conditions \(a_0 = 1\), \(a_1 = k + 1\) then leads to the result.
It follows that the number of walks of length \(m \in \N_+\) is \(w_m = n (a_m + a_{m - 1})\), and hence the right walk distribution of order \(m\) has density function \(g_m\) given by \begin{align*} g_m(x) & = \frac{a_m}{n (a_m + a_{m - 1})}, \quad x \in S \\ g_m(t) & = \frac{a_{m - 1}}{a_m + a_{m - 1}} \end{align*} It's easy to show directly that \(w_m / w_{m + 1} \to \alpha\) as \(m \to \infty\) and that \(g_m \to g\) as \(m \to \infty\) where \(\alpha\) is the rate constant and \(g\) the constant rate density function in . Of course, these results are guaranteed by the general theory in Section 5. The following examples revisit graphs that we have already studied, as a check on our work.
Suppose that \((S, \ne)\) is the complete irreflexive graph on \(n \in \N_+\) vertices. The corresponding cone graph \((S \cup \{t\}, \ne)\) is the complete irreflexive graph on \(n + 1\) vertices. For the cone graph, the uniform distribution on \(S \cup \{t\}\) has constant rate \(\alpha = 1 / n\).
Of course, we know this must be the case for a regular graph of degree \(n\).
Suppose that \((S, \lfrta)\) is the empty graph (no edges) on \(n \in \N_+\) vertices so that the corresponding cone graph is the star of order \(n\). The constant rate distribution for the star graph has rate \(\alpha = 1 / \sqrt{n}\) and density function \(g\) given by \[g(x) = \frac{1}{n + \sqrt{n}}, \; x \in S; \quad g(v) = \frac{\sqrt{n}}{n + \sqrt{n}}\]
Again, this agrees with our previous work on the star graph.
Suppose that \((S, \lfrta)\) is the (symmetric) cycle on \(n \in \{3, 4, \ldots\}\) vertices so that the corresponding cone graph is the wheel of order \(n\). The constant rate distribution for the cone graph has rate \[\alpha = \frac{\sqrt{n + 1} - 1}{n}\] and density function \(g\) given by \begin{align*} g(x) &= \frac{1}{n - 1 + \sqrt{n + 1}}, \quad x \in S \\ g(t) &= \frac{\sqrt{n + 1} - 1}{n - 1 + \sqrt{n + 1}} \end{align*}
Again, this agrees with our previous work with the wheel graph. Here are a few other examples:
Suppose that \((S, \rta)\) is the directed cycle on \(n \in \{3, 4, \ldots\}\) vertices so that the corresponding cone graph is a wheel of order \(n\) with a directed cyle. The constant rate distribution for the cone graph has rate \[\alpha = \frac{\sqrt{4 n + 1} - 1}{2 n}\] and density function \(g\) given by \begin{align*} g(x) &= \frac{2}{2 n - 1 + \sqrt{4 n + 1}}, \quad x \in S \\ g(t) &= \frac{\sqrt{4 n + 1} - 1}{2 n - 1 + \sqrt{4 n + 1}} \end{align*}
The standard cube graph \((S, \lfrta)\) has 8 vertices and is regular with degree 3. For the corresponding cone graph, the constant rate distribution has rate \[ \alpha = \frac{\sqrt{41} - 3}{16} \approx 0.2127 \] and density function \(g\) given by \[ g(x) = \frac{2}{13 + \sqrt{41}} \approx 0.1031, \; x \in S; \quad g(t) = \frac{\sqrt{41} - 3}{13 + \sqrt{41}} \approx 0.1754\]