第 91 章精校翻译:附录 B(定理 5.5 补充引理)
附录 B 定理 5.5 证明的补充引理(Additional Lemmas Related to the Proof of Theorem 5.5)
B.1 久期方程 (5.19) 的平均场解(Mean-field Solution of the Secular Equation (5.19))
B.1.1 扰动秩-2 矩阵的谱研究(Spectral Study of a Perturbed Rank-2 Matrix)
设 $A \in \mathbb{R}^{n \times n}$ 可逆,$U, V$ 为两个 $n \times m$ 矩阵。则
$$ \det\left( A + U V^{T} \right) = \det A \, \det\left( I_m + V^{T} A^{-1} U \right). $$我们对下式取行列式
$$ \left( \begin{array}{cc} A & -U \\ V^{T} & I \end{array} \right) = \left( \begin{array}{cc} A & 0 \\ V^{T} & I \end{array} \right) \left( \begin{array}{cc} I & -A^{-1} U \\ 0 & I + V^{T} A^{-1} U \end{array} \right), $$并注意到由 Schur 补公式(Horn and Johnson, 2012, Section 0.8.5)有 $\det \left( \begin{array}{cc} A & -U \\ V^{T} & I \end{array} \right) = \det I \, \det\left( A + U V^{T} \right)$。
令 $M = Z B Z^{T}$,其中 $B = \begin{pmatrix} a & b \\ b & a \end{pmatrix}$ 是一个 $2 \times 2$ 矩阵,而 $Z = \begin{pmatrix} 1_{n/2} & 0_{n/2} \\ 0_{n/2} & 1_{n/2} \end{pmatrix}$ 是一个 $n \times 2$ 矩阵。令 $m$ 为偶数。我们记 $P_{\mathcal{L}}$ 为这样的 $n \times n$ 对角矩阵:其前 $\frac{m}{2}$ 个与后 $\frac{m}{2}$ 个对角元为 1,其余元素均为 0。则
$$ \det\left( t I_n + \lambda P_{\mathcal{L}} - M \right) = t^{n-m-2} (t + \lambda)^{m-2} (t - t_1^{+}) (t - t_1^{-}) (t - t_2^{+}) (t - t_2^{-}), $$其中
$$ t_1^{\pm} = \frac{1}{2} \left( \frac{n}{2} (a + b) - \lambda \pm \sqrt{ \left( \lambda + \frac{n}{2} (a + b) \right)^2 - 2 (a + b) \lambda m } \right), $$ $$ t_2^{\pm} = \frac{1}{2} \left( \frac{n}{2} (a - b) - \lambda \pm \sqrt{ \left( \lambda + \frac{n}{2} (a - b) \right)^2 - 2 (a - b) \lambda m } \right). $$暂时假设 $t \neq -\lambda$ 且 $t \neq 0$。此时 $t I_n + \lambda P_{\mathcal{L}}$ 可逆,由引理 B.1,
$$ \begin{array}{l} \det\left( t I_n + \lambda P_{\mathcal{L}} - M \right) \ = \ \det\left( t I_n + \lambda P_{\mathcal{L}} \right) \det\left( I_2 + Z^{T} \left( t I_n + \lambda P_{\mathcal{L}} \right)^{-1} (- Z B) \right) \\ \ = \ (t + \lambda)^{m} t^{n-m} \det\left( I_2 - Z^{T} \left( t I_n + \lambda P_{\mathcal{L}} \right)^{-1} Z B \right). \end{array} \tag{B.1} $$此外,
$$ \left( t I_n + \lambda P_{\mathcal{L}} \right)^{-1} = \frac{1}{t} \left( I_n - P_{\mathcal{L}} \right) + \frac{1}{t + \lambda} P_{\mathcal{L}} = \frac{1}{t} I_n - \frac{\lambda}{t (t + \lambda)} P_{\mathcal{L}}. $$因此,我们可以写出
$$ \begin{array}{r} Z^{T} \left( t I_n + \lambda P_{\mathcal{L}} \right)^{-1} Z B = \dfrac{1}{t} Z^{T} Z B - \dfrac{\lambda}{t (t + \lambda)} Z^{T} P_{\mathcal{L}} Z B \\ = \dfrac{1}{t} \dfrac{n}{2} B - \dfrac{\lambda}{t (t + \lambda)} \dfrac{m}{2} B = x B, \end{array} $$其中 $x := \dfrac{n}{2} \dfrac{1}{t (t + \lambda)} \left( t + \lambda \left( 1 - \dfrac{m}{n} \right) \right)$。于是,直接计算行列式给出
$$ \det\left( I_2 - Z^{T} \left( t I_n + \lambda P_{\mathcal{L}} \right)^{-1} Z B \right) \ = \ \big( 1 - x (a + b) \big) \big( 1 - x (a - b) \big). $$回到方程 (B.1),我们可以写出
$$ \det\left( t I_n + \lambda P_{\mathcal{L}} - M \right) = (t + \lambda)^{m-2} t^{n-m-2} P_1(t) P_2(t), \tag{B.2} $$其中 $P_1(t) = t (t + \lambda) - \frac{n}{2} (a + b) \left( t + \lambda \left( 1 - \frac{m}{n} \right) \right)$,$P_2(t) = t (t + \lambda) - \frac{n}{2} (a - b) \left( t + \lambda \left( 1 - \frac{m}{n} \right) \right)$。由于 $t \in \mathbb{R} \mapsto \det\left( t I_n + \lambda P_{\mathcal{L}} - M \right)$ 连续(甚至解析),表达式 (B.2) 对 $t = 0$ 与 $t = -\lambda$ 同样成立(Avrachenkov et al., 2013a)。观察到下式即完成证明:
$$ P_1(t) = (t - t_1^{+}) (t - t_1^{-}) \qquad \text{与} \qquad P_2(t) = (t - t_2^{+}) (t - t_2^{-}), $$其中 $t_1^{\pm}$ 与 $t_2^{\pm}$ 如命题陈述中所定义。
设 $A$ 为满足 $p_{\mathrm{in}} > p_{\mathrm{out}} > 0$ 的[度校正随机分块模型(DC-SBM)](../glossary.html#glossary-degree-corrected-sbm)的[邻接矩阵(adjacency matrix)](../glossary.html#glossary-adjacency-matrix),$s$ 为[预言机(oracle)](../glossary.html#glossary-oracle)信息。令 $\lambda, \tau > 0$,并令 $\bar{d}_{\tau} = \frac{n}{2} \left( p_{\mathrm{in}} + p_{\mathrm{out}} \right) - n \tau$,$\bar{\alpha} = \frac{n}{2} \left( p_{\mathrm{in}} - p_{\mathrm{out}} \right)$。令 $A_{\tau} := A - \tau 1_n 1_n^{T}$,$P_{\mathcal{L}}$ 为对角矩阵,其元素 $(P_{\mathcal{L}})_{ii}$ 当且仅当 $s_i \neq 0$ 时为 1,否则为 0。则 $\mathbb{E}\widetilde{\mathcal{L}} = -\mathbb{E} A_{\tau} + \lambda \mathcal{P} - \gamma I_n$ 的谱为 $\left\{ -\gamma - t_1^{\pm} ;\, -\gamma - t_2^{\pm} ;\, -\gamma ;\, -\gamma + \lambda ;\, 0 \right\}$,其中
$$ t_1^{\pm} = \frac{1}{2} \left( \bar{d}_{\tau} - \lambda \pm \sqrt{ \left( \lambda + \bar{d}_{\tau} \right)^2 - 4 \bar{d}_{\tau} \lambda \left( \eta_1 + \eta_0 \right) } \right), $$ $$ t_2^{\pm} = \frac{1}{2} \left( \bar{\alpha} - \lambda \pm \sqrt{ \left( \lambda + \bar{\alpha} \right)^2 - 4 \bar{\alpha} \lambda \left( \eta_1 + \eta_0 \right) } \right). $$令 $M = \begin{pmatrix} p_{\mathrm{in}} - \tau & p_{\mathrm{out}} - \tau \\ p_{\mathrm{out}} - \tau & p_{\mathrm{in}} - \tau \end{pmatrix}$,$Z = \begin{pmatrix} 1_{n/2} & 0_{n/2} \\ 0_{n/2} & 1_{n/2} \end{pmatrix}$。我们注意到 $\mathbb{E} A_{\tau} = Z M Z^{T}$,于是可以应用命题 B.1 来计算 $\mathbb{E}\widetilde{\mathcal{L}}$ 的特征多项式。对 $x \in \mathbb{R}$,$\det\left( \mathbb{E}\widetilde{\mathcal{L}} - x I_n \right) = \det\left( (-\gamma - x) I_n - \mathbb{E} A_{\tau} + \lambda \mathcal{P} \right)$,其根为 $-\gamma - t_1^{\pm}$、$-\gamma - t_2^{\pm}$、$-\gamma$ 与 $-\gamma + \lambda$。
B.1.2 $\bar{\gamma}_{*}$ 的估计(Estimation of $\bar{\gamma}_{*}$)
设 $\bar{\gamma}_{*}$ 为平均场模型下方程 (5.19) 的解。则
$$ - \bar{\alpha} (1 - 2 \eta_0) \ \leq \ \bar{\gamma}_{*} \ \leq \ - \bar{\alpha}. $$对 $\lambda \geq 0$,我们记 $(\bar{x}_{\lambda}, \bar{\gamma}_{*}(\lambda))$ 为平均场 DC-SBM 上方程组 (5.17) 的解。证明分两步。首先,让我们证明 $\bar{\gamma}_{*}(0) = -\bar{\alpha}$ 且 $\bar{\gamma}_{*}(\infty) = -\bar{\alpha} (1 - 2\eta_0)$。当 $\lambda = 0$ 时,带约束线性方程组 (5.17) 退化为一个特征向量问题,因此 $\bar{\gamma}_{*}(0)$ 等于 $-\alpha$,即 $-\mathbb{E} A_{\tau}$ 的最小特征值。
此外,当 $\lambda = \infty$ 时,硬约束 $x_{\ell} = \bar{s}_{\ell}$ 被强制执行,方程组 (5.17) 变为
$$ \left\{ \begin{array}{ll} \left( -\mathbb{E} A_{\tau} - \bar{\gamma}_{*}(\infty) I_n \right)_{uu} \bar{x}_{u} = \left( \mathbb{E} A_{\tau} \right)_{u\ell} \bar{s}_{\ell} \\ \bar{x}_{u}^{T} \bar{x}_{u} = n (1 - \eta_0 - \eta_1) \end{array} \right. $$并且我们用手算验证,$\bar{\gamma}_{*}(\infty) = -\bar{\alpha} (1 - 2\eta_0)$ 连同 $\bar{x}_{u} = Z_{u}$ 确实是解。
学习笔记补全这一手算验证其次,若令 $C_{\lambda}(\boldsymbol{x}) = -\boldsymbol{x}^{T} \mathbb{E} A_{\tau} \boldsymbol{x} + \lambda (\bar{s} - \mathcal{P} \boldsymbol{x})^{T} (\bar{s} - \mathcal{P} \boldsymbol{x})$ 为 (5.14) 中被最小化的代价函数,则由方程 (5.17) 我们有 $\bar{\gamma}_{*}(\lambda_1) - \bar{\gamma}_{*}(\lambda_2) = C_{\lambda_1}(\bar{x}_1) - C_{\lambda_2}(\bar{x}_2) + \lambda_1 \bar{x}_1^{T} \bar{s} - \lambda_2 \bar{x}_2^{T} \bar{s}$。由于 $\lambda \mapsto C_{\lambda}(x)$ 递增,$\lambda_1 \leq \lambda_2$ 蕴含 $C_{\lambda_1}(\bar{x}_1) \leq C_{\lambda_2}(\bar{x}_2)$。由于 $\bar{x}_{\lambda}^{T} \bar{s} \geq 0$(若不然,则 $C_{\lambda}(-\bar{x}_{\lambda}) \leq C_{\lambda}(\bar{x}_{\lambda})$,从而 $\bar{x}_{\lambda} \neq \arg\min_{x \in \mathbb{R}^n} C_{\lambda}(x)$),我们可以断定 $\bar{\gamma}_{*}(0) \leq \bar{\gamma}_{*}(\lambda)$,且 $\bar{\gamma}_{*}(\lambda) \leq \bar{\gamma}_{*}(\infty)$。
B.1.3 $\gamma_{*}$ 的集中(Concentration of $\gamma_{*}$)
设 $\gamma_{*}$ 与 $\bar{\gamma}_{*}$ 分别为 DC-SBM 与平均场 DC-SBM 下方程 (5.17) 的解。则
$$ | \gamma_{*} - \bar{\gamma}_{*} | \ \leq \ \left( 1 + \frac{27 \left( \bar{\alpha} + \lambda \right)^3}{\sqrt{2}\, \sqrt{\eta_1 + \eta_0}\, (\eta_1 - \eta_0)\, \bar{\alpha}^2 \lambda} \right) \sqrt{\bar{d}}. $$方程 (5.19) 左端关于 $(\bar{\delta}_1, \ldots, \bar{\delta}_n, \bar{b}_1, \ldots, \bar{b}_n, \gamma)$ 的梯度等于
$$ 2 \sum_{i=1}^{n} \frac{\bar{b}_i}{\bar{\delta}_i - \bar{\gamma}} \left[ \frac{\Delta b_i}{\bar{\delta}_i - \bar{\gamma}_{*}} - \frac{\bar{b}_i \Delta \delta_i}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^2} + \frac{\bar{b}_i \Delta \gamma}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^2} \right]. $$于是,我们有
$$ \Delta \gamma \sum_{i=1}^{n} \frac{\bar{b}_i^2}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^3} = \sum_{i=1}^{n} \frac{\bar{b}_i^2}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^3} \Delta \delta_i - \sum_{i=1}^{n} \frac{\bar{b}_i}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^2} \Delta b_i + o\left( \Delta \delta_i, \Delta b_i \right). $$首先我们看到,对所有 $i \in [n]$,由 DC-SBM 图邻接矩阵的集中性有 $\Delta \delta_i = \left| \delta_i - \bar{\delta}_i \right| \leq \| A - \mathbb{E} A \| \leq \bar{d}$。因此,利用这一事实与 $\bar{\gamma}_{*} \leq \bar{\delta}_1 \leq \bar{\delta}_2 \leq \cdots \leq \bar{\delta}_n$,
$$ \begin{array}{rcl} \Delta \gamma & = & | \gamma_{*} - \bar{\gamma}_{*} | \ \leq \ \max\limits_{i} \left| \delta_i - \bar{\delta}_i \right| + \dfrac{ \max\limits_{i} \dfrac{1}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^2} }{ \min\limits_{i} \dfrac{1}{\left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^3} } \, \dfrac{ \sum_{i} | \bar{b}_i | \cdot | b_i - \bar{b}_i | }{ \sum_{i} \bar{b}_i^2 } \\ & & \leq \ \sqrt{\bar{d}} + \dfrac{ \max\limits_{i} \left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^3 }{ \min\limits_{i} \left( \bar{\delta}_i - \bar{\gamma}_{*} \right)^2 } \, \dfrac{ \sum_{i} | \bar{b}_i | \cdot | b_i - \bar{b}_i | }{ \sum_{i} \bar{b}_i^2 }. \end{array} $$我们注意到 $\min_i \left| \bar{\delta}_i - \bar{\gamma}_{*} \right| = \bar{\delta}_1 - \bar{\gamma}_{*}$。利用引理 B.3 与推论 B.2 给出的 $\bar{\delta}_1$ 的表达式,我们有
$$ \min_{i} \left| \bar{\delta}_i - \bar{\gamma}_{*} \right| \geq \bar{\alpha} + \lambda. $$类似地,$\max_i \left| \bar{\delta}_i - \bar{\gamma}_{*} \right| = \bar{\delta}_n - \bar{\gamma}_{*} = \bar{\delta}_n - \bar{\delta}_1 + \bar{\delta}_1 - \bar{\gamma}_{*}$。推论 B.2 给出 $\bar{\delta}_n = \lambda$ 与 $\bar{\delta}_1 = \frac{1}{2} \left( \lambda - \bar{\alpha} - \sqrt{ \left( \lambda + \bar{\alpha} \right)^2 - 4 \bar{\alpha} \lambda \left( \eta_0 + \eta_1 \right) } \right)$,因此 $\bar{\delta}_n - \bar{\delta}_1 \leq \bar{\alpha} + \lambda$。于是,利用引理 B.3,
$$ \max_{i} \left| \bar{\delta}_i - \bar{\gamma}_{*} \right| \leq \frac{3}{2} \left( \bar{\alpha} + \lambda \right). $$因此,我们有
$$ | \gamma_{*} - \bar{\gamma}_{*} | \ \leq \ \sqrt{\bar{d}} + \frac{27}{8} \left( \bar{\alpha} + \lambda \right) \cdot \frac{ \sum_{i} | \bar{b}_i | \cdot | b_i - \bar{b}_i | }{ \sum_{i} \bar{b}_i^2 }. \tag{B.3} $$$\dfrac{ \sum_{i} | \bar{b}_i | \cdot | b_i - \bar{b}_i | }{ \sum_{i} \bar{b}_i^2 }$ 这一项可以如下界定。令 $\mathcal{I} = \left\{ i \in [n] : \bar{b}_i \neq 0 \right\}$。则
$$ \sum_{i} | \bar{b}_i | \cdot | b_i - \bar{b}_i | \ \leq \ \max_{i \in \mathcal{I}} | b_i - \bar{b}_i | \cdot \sum_{i \in \mathcal{I}} \left| \bar{b}_i \right|. $$把 Cauchy–Schwarz 不等式
$$ \left| b_i - \bar{b}_i \right| = \lambda \left| \left( Q_{\cdot i} - \bar{Q}_{\cdot i} \right)^{T} \bar{s} \right| \leq \lambda \left\| Q_{\cdot i} - \bar{Q}_{\cdot i} \right\|_2 \cdot \| \bar{s} \|, $$与 Davis–Kahan 定理(Yu et al., 2015)
$$ \left\| Q_{\cdot i} - \bar{Q}_{\cdot i} \right\|_2 \ \leq \ \frac{ 2^{3/2} \left\| A - \mathbb{E} A \right\| }{ \min \left\{ \bar{\delta}_i - \bar{\delta}_{i-1},\, \bar{\delta}_{i+1} - \bar{\delta}_i \right\} }, $$以及 $\| \bar{s} \| = \sqrt{ (\eta_0 + \eta_1) n }$ 和 $A$ 向 $\mathbb{E} A$ 的集中性相结合,得到
$$ \max_{i \in \mathcal{I}} | b_i - \bar{b}_i | \ \leq \ \frac{ \lambda \sqrt{ (\eta_0 + \eta_1) n } }{ \min\limits_{i \in \mathcal{I}} \left\{ \bar{\delta}_i - \bar{\delta}_{i-1},\, \bar{\delta}_{i+1} - \bar{\delta}_i \right\} } \cdot 2^{3/2} \sqrt{\bar{d}}. $$利用引理 B.4,我们看到 $\mathcal{I} = \left\{ i \in [n] : \delta_i \notin \left\{ 0, t_1^{-} \right\} \right\}$。把它与推论 B.2 相结合,给出
$$ \begin{array}{rl} \min\limits_{i \in \mathcal{I}} \left\{ \bar{\delta}_i - \bar{\delta}_{i-1},\, \bar{\delta}_{i+1} - \bar{\delta}_i \right\} = \lambda + t_2^{+} & \\ = \dfrac{\alpha + \lambda}{2} \left( 1 - \sqrt{ 1 - 4 \dfrac{\alpha \lambda}{\left( \alpha + \lambda \right)^2} \left( \eta_0 + \eta_1 \right) } \right) & \\ \geq \dfrac{\alpha \lambda}{\alpha + \lambda} \left( \eta_0 + \eta_1 \right), & \end{array} $$其中我们使用了 $\sqrt{1 - x} \leq 1 - x / 2$。
因此,
$$ \sum_{i} \left| \bar{b}_i \right| \cdot \left| b_i - \bar{b}_i \right| \ \leq \ 2^{3/2} \sqrt{ \frac{n \bar{d}}{\eta_0 + \eta_1} } \cdot \frac{\alpha + \lambda}{\alpha} \cdot \sum_{i} \left| \bar{b}_i \right|. $$注意到 $\sum_{i} \bar{b}_i^2 \geq \left( \sum_{i} \left| \bar{b}_i \right| \right)^2 \geq \left| \bar{b}_1 \right| \cdot \sum_{i} \left| \bar{b}_i \right| \geq \sqrt{n}\, \frac{\eta_1 - \eta_0}{2}\, \frac{\bar{\alpha} \lambda}{\lambda + \bar{\alpha}} \sum_{i} \left| \bar{b}_i \right|$,其中我们使用了 $\bar{b}_1 \geq \sqrt{n}\, \frac{\eta_1 - \eta_0}{2}\, \frac{\bar{\alpha} \lambda}{\lambda + \bar{\alpha}}$(引理 B.4),我们有
$$ \frac{ \sum_{i} \left| \bar{b}_i \right| \cdot \left| b_i - \bar{b}_i \right| }{ \sum_{i} \bar{b}_i^2 } \ \leq \ \frac{2^{5/2}}{ (\eta_1 - \eta_0) \sqrt{\eta_1 + \eta_0} }\, \frac{ (\alpha + \lambda)^2 }{ \alpha^2 \lambda } \sqrt{\bar{d}}. $$回到不等式 (B.3),这蕴含
$$ | \gamma_{*} - \bar{\gamma}_{*} | \ \leq \ \left( 1 + \frac{27 \left( \bar{\alpha} + \lambda \right)^3}{\sqrt{2}\, \sqrt{\eta_1 + \eta_0}\, (\eta_1 - \eta_0)\, \bar{\alpha}^2 \lambda} \right) \sqrt{\bar{d}}. $$令 $-\mathbb{E} A_{\tau} + \lambda \mathcal{P} = \bar{Q} \bar{\Delta} \bar{Q}^{T}$,其中 $\bar{\Delta} = \mathrm{diag}\left( \bar{\delta}_1, \ldots, \bar{\delta}_n \right)$ 且 $\bar{Q}^{T} \bar{Q} = I_n$。记 $\bar{b} = \lambda \bar{Q}^{T} s$。我们有 $\bar{b}_1 \geq \sqrt{n}\, \frac{\lambda (\eta_1 - \eta_0)}{2}\, \frac{\bar{\alpha}}{\lambda + \bar{\alpha}}$。此外,若 $\bar{\delta}_i = 0$ 或 $\bar{\delta}_i = -t_1^{-}$,则 $\bar{b}_i = 0$。
首先,由推论 B.2,
$$ \bar{\delta}_1 = -t_2^{+} = -\frac{1}{2} \left( \bar{\alpha} - \lambda + \sqrt{ \left( \lambda + \bar{\alpha} \right)^2 - 4 \bar{\alpha} \lambda \left( \eta_1 + \eta_0 \right) } \right). $$由对称性,(与 $\bar{\delta}_1$ 相应的)第一个特征向量 $\bar{Q}_{\cdot 1}$ 的第 $i$ 个分量等于
$$ \left\{ \begin{array}{ll} \nu_1 Z_i \quad & \text{若 } i \in [\ell], \\ \nu_0 Z_i \quad & \text{若 } i \notin [\ell], \end{array} \right. $$其中 $\nu_1$ 与 $\nu_0$ 待定。于是,方程 $\left( -\mathbb{E} A_{\tau} + \lambda \mathcal{P} \right) \bar{Q}_{\cdot 1} = \bar{\delta}_1 \bar{Q}_{\cdot 1}$ 导出
$$ \left\{ \begin{array}{r} \bar{\alpha} \left( (\eta_1 + \eta_0) \nu_1 + (1 - \eta_1 - \eta_0) \nu_0 \right) = -t_2^{+} \nu_0 \\ \bar{\alpha} \left( (\eta_1 + \eta_0) \nu_1 + (1 - \eta_1 - \eta_0) \nu_0 \right) + \lambda \nu_1 = -t_2^{+} \nu_1, \end{array} \right. $$结合范数约束 $\| \nu \|_2 = 1$,得到
$$ \left\{ \begin{array}{ll} \nu_1 = \dfrac{1}{\sqrt{n}}\, \dfrac{t_2^{+}}{ \sqrt{ \left( \eta_1 + \eta_0 \right) \left( t_2^{+} \right)^2 + \left( 1 - \eta_1 - \eta_0 \right) \left( t_2^{+} + \lambda \right)^2 } }, \\ \nu_0 = \dfrac{1}{\sqrt{n}}\, \dfrac{+ t_2^{+} + \lambda}{ \sqrt{ \left( \eta_1 + \eta_0 \right) \left( t_2^{+} \right)^2 + \left( 1 - \eta_1 - \eta_0 \right) \left( t_2^{+} + \lambda \right)^2 } }. \end{array} \right. $$由于 $\bar{b}_1 = \lambda \nu^{T} \bar{s} = \lambda (\eta_1 - \eta_0) n \nu_1$,我们有
$$ \frac{\bar{b}_1}{\sqrt{n}} \ = \ \lambda (\eta_1 - \eta_0)\, \frac{t_2^{+}}{ \sqrt{ \left( \eta_1 + \eta_0 \right) \left( t_2^{+} \right)^2 + \left( 1 - \eta_1 - \eta_0 \right) \left( t_2^{+} + \lambda \right)^2 } }. $$注意到 $t_2^{+} \geq \frac{\bar{\alpha}}{2}$ 且 $t_2^{+} \leq \bar{\alpha}$,证明即告完成。事实上,
$$ \begin{array}{rl} \dfrac{\bar{b}_1}{\sqrt{n}} \geq \lambda (\eta_1 - \eta_0)\, \dfrac{\bar{\alpha}}{ 2 \sqrt{ (\eta_1 + \eta_0)\, \bar{\alpha}^2 + (1 - \eta_1 - \eta_0)\, (\bar{\alpha} + \lambda)^2 } } & \\ \geq \dfrac{\lambda (\eta_1 - \eta_0)}{2}\, \dfrac{\bar{\alpha}}{ (\bar{\alpha} + \lambda) \sqrt{ (\eta_1 + \eta_0) \left( \frac{\bar{\alpha}}{\bar{\alpha} + \lambda} \right)^2 + 1 - \eta_1 - \eta_0 } } & \\ \geq \dfrac{\lambda (\eta_1 - \eta_0)}{2}\, \dfrac{\bar{\alpha}}{\lambda + \bar{\alpha}}. & \end{array} $$这证明了本引理的第一个论断。
类似地,由对称性,与 $-t_1^{-}$ 相应的特征向量 $\nu^{\prime}$ 的第 $i$ 个分量在 $i \in \ell$ 时等于 $\nu^{\prime}_{\ell}$,否则等于 $\nu^{\prime}_{u}$,因此 $(\nu^{\prime})^{T} s = 0$。
最后,令 $I_0 := \left\{ i \in [n] : \bar{\delta}_i = 0 \right\}$。由推论 B.2,我们有 $|I_0| = n (1 - \eta_1 - \eta_0) - 2$。由于 $0$ 也是提取的子矩阵 $\left( -\mathbb{E} A_{\tau} + \lambda \mathcal{P} \right)_{u,u} = \left( -\mathbb{E} A_{\tau} \right)_{u,u}$ 的 $n (1 - \eta_0 - \eta_1) - 2$ 阶特征值,对所有 $k \in I_0$ 与每个 $i \in [n]$ 都有 $\bar{Q}_{ik} = 0$。因此,对 $k \in I_0$,$b_k = \lambda \bar{Q}_{\cdot k}^{T} s = 0$。
B.2 带约束线性方程组 (5.17) 的平均场解(Mean-field Solution of the Constrained Linear System (5.17))
在本节中,我们计算平均场模型的解 $\bar{x}$,并由此导出恢复簇的条件。
设 $\tau > p_{\mathrm{out}}$。则平均场 DC-SBM 下方程 (5.18) 的解为向量 $\bar{x}$,其元素 $\bar{x}_i$ 由下式给出
$$ \bar{x}_i = \left\{ \begin{array}{ll} C \left( -1 + (\eta_1 - \eta_0) \bar{\alpha} B \right) Z_i, & \text{若 } i \in \ell \text{ 且 } s_i \neq Z_i, \\ C \left( 1 + (\eta_1 - \eta_0) \bar{\alpha} B \right) Z_i, & \text{若 } i \in \ell \text{ 且 } s_i = Z_i, \\ \dfrac{-\bar{\alpha} C}{ \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \bar{\gamma}_{*} } (\eta_1 - \eta_0) \left( 1 + (\eta_1 + \eta_0) \bar{\alpha} B \right) Z_i, & \text{若 } i \notin \ell, \end{array} \right. $$其中 $\bar{\alpha} = \frac{n}{2} \left( p_{\mathrm{in}} - p_{\mathrm{out}} \right)$,$B = \dfrac{ \bar{\alpha} \bar{\gamma}_{*} }{ \lambda \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \bar{\gamma}_{*} \left( \lambda - \bar{\alpha} - \bar{\gamma}_{*} \right) }$,$C = \dfrac{\lambda}{\lambda - \bar{\gamma}_{*}}$。
令 $\bar{x}$ 为方程 (5.18) 的一个解。由对称性,我们有
$$ \bar{x}_i = \left\{ \begin{array}{ll} x_t Z_i, \quad & \text{若 } i \in [\ell] \text{ 且 } \bar{s}_i = Z_i, \\ x_f Z_i, \quad & \text{若 } i \in [\ell] \text{ 且 } \bar{s}_i = -Z_i, \\ x_0 Z_i, \quad & \text{若 } i \notin [\ell], \end{array} \right. $$其中 $x_t$、$x_f$ 与 $x_0$ 为待定未知量。由于对每个 $i \in [n]$ 都有
$$ \left( \mathbb{E} A_{\tau} \bar{x} \right)_i = \bar{\alpha} \left( x_0 (1 - \eta_1 - \eta_0) + x_t \eta_1 + x_f \eta_0 \right), $$由对所有 $i \in [n]$ 的方程 $\left( \left( -\mathbb{E} A_{\tau} + \lambda \mathcal{P} - \bar{\gamma}_{*} I_n \right) \bar{x} \right)_i = \lambda s_i$ 组成的线性方程组导出方程组
$$ \left\{ \begin{array}{c} -\bar{\alpha} \left( (1 - \eta_1 - \eta_0) x_0 + x_t \eta_1 + x_f \eta_0 \right) - \bar{\gamma}_{*} x_0 = 0, \\ -\bar{\alpha} \left( (1 - \eta_1 - \eta_0) x_0 + x_t \eta_1 + x_f \eta_0 \right) - \bar{\gamma}_{*} x_t + \lambda x_t = \lambda, \\ -\bar{\alpha} \left( (1 - \eta_1 - \eta_0) x_0 + x_t \eta_1 + x_f \eta_0 \right) - \bar{\gamma}_{*} x_f + \lambda x_f = -\lambda. \end{array} \right. $$后一方程组的各行分别对应一个未被预言机标注的节点、被正确标注的节点与被错误标注的节点。该方程组可以改写如下:
$$ \left\{ \begin{array}{c} x_0 = \dfrac{-\bar{\alpha}}{ \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \bar{\gamma}_{*} } \left( \eta_1 x_t + \eta_0 x_f \right), \\ \bar{\gamma}_{*} x_0 + x_t \left( \lambda - \bar{\gamma}_{*} \right) = \lambda, \\ \bar{\gamma}_{*} x_0 + x_f \left( \lambda - \bar{\gamma}_{*} \right) = -\lambda. \end{array} \right. $$特别地,我们有 $x_t - x_f = \dfrac{2\lambda}{\lambda - \bar{\gamma}_{*}}$。随后在方程 $\bar{\gamma}_{*} x_0 + x_f \left( \lambda - \bar{\gamma}_{*} \right) = -\lambda$ 中依次消去 $x_0$ 与 $x_t$,我们求得
$$ x_f = \frac{\lambda}{\lambda - \bar{\gamma}_{*}} \left( -1 + \frac{ \bar{\alpha} \bar{\gamma}_{*} (\eta_1 - \eta_0) }{ \lambda \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \lambda \bar{\gamma}_{*} - \bar{\gamma}_{*} \left( \bar{\alpha} + \bar{\gamma}_{*} \right) } \right), $$ $$ x_t = \frac{\lambda}{\lambda - \bar{\gamma}_{*}} \left( 1 + \frac{ \bar{\alpha} \bar{\gamma}_{*} (\eta_1 - \eta_0) }{ \lambda \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \lambda \bar{\gamma}_{*} - \bar{\gamma}_{*} \left( \bar{\alpha} + \bar{\gamma}_{*} \right) } \right), $$最后
$$ \begin{array}{l} x_0 = \dfrac{-\bar{\alpha}}{ \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \bar{\gamma}_{*} } \\ \quad \cdot \dfrac{\lambda}{\lambda - \bar{\gamma}_{*}} \left( 1 + \frac{ \bar{\alpha} \bar{\gamma}_{*} (\eta_1 + \eta_0) }{ \lambda \bar{\alpha} \left( 1 - \eta_1 - \eta_0 \right) + \lambda \bar{\gamma}_{*} - \bar{\gamma}_{*} \left( \bar{\alpha} + \bar{\gamma}_{*} \right) } \right). \end{array} $$设 $\tau > p_{\mathrm{out}}$。若满足下列任一条件,则 $\mathrm{sign}\left( \bar{x}_i \right) = \mathrm{sign}\left( Z_i \right)$:
- 节点 $i$ 未被预言机标注;
- 节点 $i$ 被预言机正确标注;
- 节点 $i$ 被预言机错误标注,且 $\lambda < (1 - 2\eta_0)\, \bar{\alpha}\, \dfrac{\eta_1 - \eta_0}{\eta_1 + \eta_0}$。
若 $\bar{x}_i$ 的符号等于 $Z_i$ 的符号,则节点 $i$ 被判决规则 (5.16) 正确分类。利用附录 B.1.2 中的引理 B.3,我们有 $-\bar{\alpha} \leq \bar{\gamma}_{*} \leq -\bar{\alpha} (1 - 2\eta_0)$。因此,命题 B.3 中的量 $B$ 与 $C$ 满足 $C \geq 0$ 与 $\dfrac{1 - 2\eta_0}{\lambda (\eta_0 + \eta_1)} \leq B \leq \dfrac{1}{\lambda (\eta_0 + \eta_1)}$。于是,结论由命题 B.3 中算得的 $\bar{x}_i$ 的表达式即得。