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Almost Exact Recovery in Label Spreading

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11631)


In semi-supervised graph clustering setting, an expert provides cluster membership of few nodes. This little amount of information allows one to achieve high accuracy clustering using efficient computational procedures. Our main goal is to provide a theoretical justification why the graph-based semi-supervised learning works very well. Specifically, for the Stochastic Block Model in the moderately sparse regime, we prove that popular semi-supervised clustering methods like Label Spreading achieve asymptotically almost exact recovery as long as the fraction of labeled nodes does not go to zero and the average degree goes to infinity.


  • Semi-supervised clustering
  • Community detection
  • Label spreading
  • Random graphs
  • Stochastic Block Model

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This work has been done within the project of Inria – Nokia Bell Labs “Distributed Learning and Control for Network Analysis”.

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Correspondence to Maximilien Dreveton .

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A Background Results on Matrix Analysis

1.1 A.1 Inversion of the Identity Matrix Minus a Rank 2 Matrix

Lemma 1

(Sherman-Morrison-Woodbury formula). Let A be an invertible \(n \times n\) matrix, and BCD matrices of correct sizes. Then: \(\Big ( A + BCD \Big )^{-1} = A^{-1} -A^{-1} B \Big (I + CDA^{-1} B \Big )^{-1} CDA^{-1}\). In particular, if uv are two column vectors of size \(n\times 1\), we have: \(\Big ( A + u v^T \Big )^{-1} = A^{-1} - \dfrac{A^{-1}u v^T A^{-1} }{1 + v^T A^{-1} u}\).


See for example [10], section 0.7.4.   \(\square \)

Lemma 2

Let \(M = \begin{pmatrix} aJ_{n_1} &{} b J_{n_1 n_2} \\ c J_{n_2 n_1} &{} d J_{n_2} \end{pmatrix}\) for some values abcd. Let \(n = n_1+n_2\). If \(I_n-M\) is invertible, we have:

$$\begin{aligned} (I-M)^{-1} = I_n - \dfrac{1}{K} \begin{pmatrix} \big (-a + n_2(ad-bc) \big ) J_{n_1} &{} -b J_{n_1 n_2} \\ -c J_{n_2 n_1} &{} \big ( -d + n_1(ad-bc) \big ) J_{n_2} \end{pmatrix} \end{aligned}$$

where \(K = (1-n_1a)(1-n_2d) - n_1n_2 bc\).


We will use the Sherman-Morrison-Woodbury matrix identity (Lemma 1) with \(A = I_n\), \(D = \begin{pmatrix} 1\dots 1; 0 \dots 0 \\ 0 \dots 0 ; 1 \dots 1 \end{pmatrix}\) (on the first line, there are \(n_1\) ones and \(n_2\) zeros), \(B = D^T\) and \(C = \begin{pmatrix} -a &{} -b \\ -c &{} -d \end{pmatrix}\). We can easily verify that \(BCD = -M\).

$$\begin{aligned} (I-M)^{-1}&= I_n - B(I + CDB)^{-1} CD \\&= I_n - B \begin{pmatrix} 1-n_1a &{} -n_2 b \\ -n_1c &{} 1-n_2d \\ \end{pmatrix}^{-1} CD \\&= I_n - B \dfrac{1}{(1-n_1a)(1-n_2d) - n_1n_2 bc }\begin{pmatrix} 1-n_2d &{} n_2 b \\ n_1c &{} 1-n_1a \\ \end{pmatrix} CD \\&= I_n - \dfrac{1}{K} B \begin{pmatrix} -a + n_2(ad-bc) &{} -b \\ -c &{} -d + n_1(ad-bc) \end{pmatrix} D \\&= I_n - \dfrac{1}{K} \begin{pmatrix} \big (-a + n_2(ad-bc) \big ) J_{n_1} &{} -b J_{n_1 n_2} \\ -c J_{n_2 n_1} &{} \big ( -d + n_1(ad-bc) \big ) J_{n_2} \end{pmatrix}. \end{aligned}$$

   \(\square \)

1.2 A.2 Spectral Study of a Rank 2 Matrix

Lemma 3

(Schur’s determinant identity, [10]). Let AD and \(\begin{pmatrix} A &{} B \\ C &{} D \end{pmatrix}\) be squared matrices. If A is invertible, we have:

$$\det \begin{pmatrix} A &{} B \\ C &{} D \end{pmatrix} = \det ( A ) \det (D - CA^{-1}B).$$


Follows from the formula \(\begin{pmatrix} A &{} B \\ C &{} D \end{pmatrix} = \begin{pmatrix} A &{} 0 \\ C &{} I_q \end{pmatrix} \begin{pmatrix} I_p &{} A^{-1}B \\ 0 &{} D-CA^{-1}B \end{pmatrix}\).    \(\square \)

Lemma 4

(Matrix determinant lemma, [10]). For an invertible matrix A and two column vectors u and v, we have \(\det (A + uv^T)= (1+v^T A^{-1} u ) \det (A)\).

Lemma 5

Let \(\alpha \) and \(\beta \) be two constants. When \(M = \alpha I_n + \beta J\) where J is the \(n \times n\) matrix with all entries equal to one, we have \(\det M = \alpha ^{n-1}(\alpha + \beta n)\).


Suppose that \(\alpha \not =0\). Then with \(v^T = (1,\dots , 1)\) and \(u = \beta (1,\dots ,1)\) vectors of size \(1 \times n\), Lemma 4 gives us

$$\begin{aligned} \det M&= \det (\alpha I_n) \Big (1 + v^T (\alpha I_n)^{-1} u \Big ) \\&= \alpha ^n \Big (1+ \dfrac{\beta n}{\alpha } \Big ) \\&= \alpha ^{n-1}(\alpha + \beta n), \end{aligned}$$

which proves the lemma for \(\alpha \not =0\). To treat the case \(\alpha =0\), see that the function \(\alpha \in \mathbf {R}\mapsto \det (\alpha I_n + \beta J)\) is continuous (even analytic) [4], thus by continuous prolongation in \(\alpha =0\), the expression \(\alpha ^{n-1}(\alpha + \beta n)\) holds for any \(\alpha \in \mathbf {R}\).   \(\square \)

Proposition 2

Let \(M = \begin{pmatrix} aJ_{n_1} &{} b J_{n_1 n_2} \\ c J_{n_2 n_1} &{} d J_{n_2} \end{pmatrix}\) for some values abcd. The eigenvalues of M are:

  • 0 with multiplicity \(n_1 + n_2-2\);

  • \(\lambda _\pm = \dfrac{1}{2} \big (n_1a + n_2d \pm \sqrt{\varDelta } \big ) \) where \(\varDelta = (n_1a-n_2d)^2 + 4n_1 n_2 bc \).


The matrix being of rank 2 (except for some degenerate cases), the fact that 0 is an eigenvalue of multiplicity \(n_1+n_2-2\) is obvious. By an explicit computation of the characteristic polynomial of M, the two remaining eigenvalues will be given as roots of a polynomial of degree 2.

Let \(\lambda \in \mathbf {R}\) and \(A := \lambda I_{n_1} - aJ_{n_1}\). If \(\lambda \not \in \{0; a n_1 \}\), then A is invertible and by the Schur’s determinant identity (Lemma 3) we have

$$\begin{aligned} \det (\lambda I_n - M)&= \det A \, \det \Big ( \lambda I_{n_2} - dJ_{n_2} - cJ_{n_2 n_1} A^{-1} b J_{n_1 n_2} \Big ) \\&= \det A \, \det B. \end{aligned}$$

From Lemma 5, it follows that \(\det A = \lambda ^{n_1-1} \big (\lambda - n_1a)\).

Let us now compute \(\det B\). First, we show that \(A^{-1} = \dfrac{1}{\lambda } \Big (I_{n_1} + \dfrac{a}{\lambda - a n_1} J_{n_1} \Big )\). Indeed, from the Sherman-Morrison-Woodbury formula (Lemma 1) with \(u=-a 1_{n_1}\) and \(v= 1_{n_1}\), it follows that

$$\begin{aligned} \Big (\lambda I_{n_1} - aJ_{n_1} \Big )^{-1}&= \dfrac{1}{\lambda } I_{n_1} - \dfrac{1}{\lambda ^2} \dfrac{ -aJ_{n_1} }{ 1 + \dfrac{-a n_1 }{\lambda }} \\&= \dfrac{1}{\lambda } I_{n_1} + \dfrac{1}{\lambda } \, \dfrac{a}{\lambda -an_1} J_{n_1}, \end{aligned}$$

which gives the desired expression. Thus,

$$\begin{aligned} B&= \lambda I_{n_2} - dJ_{n_2} - \dfrac{bc}{\lambda } J_{n_2 n_1} \big ( I_{n_1} + \dfrac{a}{\lambda - a n_1} J_{n_1} \big ) J_{n_1 n_2} \\&= \lambda I_{n_2} - dJ_{n_2} - \dfrac{bc}{\lambda } \big ( n_1 + \dfrac{a \, n_1^2}{\lambda - an_1} \big )J_{n_2} \\&= \lambda I_{n_2} + \Big ( - d - \dfrac{bc n_1}{\lambda - an_1} \Big ) J_{n_2}. \end{aligned}$$

Again, this matrix is of the form \(\lambda I_n + \beta J_n\), and we can use Lemma 5 to show that

$$\begin{aligned} \det B = \lambda ^{n_2-1} \Big ( \lambda + n_2 \beta \Big ). \end{aligned}$$

Now we can finish the computation of \(\det (\lambda I_n - M)\)

$$\begin{aligned} \det (\lambda I_n - M)&= \lambda ^{n_1+n_2-2} \big ( \lambda - n_1 a \big ) \Big ( \lambda - n_2 d - \dfrac{bc n_1 n_2}{ \lambda - a n_1} \Big ) \\&= \lambda ^{n_1+n_2-2} \Big ( \lambda ^2 + \lambda (-n_1 a - n_2d) + n_1 n_2 (ad-bc) \Big ). \end{aligned}$$

The discriminant of this second degree polynomial expression is given by

$$\begin{aligned} \varDelta&= (n_1 a + n_2d)^2 - 4 n_1 n_2 (ad-bc) \\&= (n_1a-n_2d)^2 + 4 n_1 n_2 bc. \end{aligned}$$

Thus \(\varDelta \ge 0\) and the two remaining eigenvalues are given by

$$ \lambda _\pm = \dfrac{1}{2} \big (n_1a + n_2d \pm \sqrt{\varDelta } \big ). $$

   \(\square \)

1.3 A.3 Spectral Study of \(\mathbf {E}\mathcal L\)

Proposition 3

(Eigenvalues of \(\mathbf {E}\mathcal L_{uu}\), symmetric case). Assume two communities of equal size, with \(p_1 = p_2 (= p)\). The two smallest eigenvalues of \(\mathbf {E}\mathcal L_{uu}\) are:

$$\begin{aligned} \lambda _1 = r \, \dfrac{p - q}{p+q} \quad \text {and} \quad \lambda _2 = r . \end{aligned}$$

Note that the other eigenvalue of \(\mathbf {E}\mathcal L_{uu}\) is one (with multiplicity \(\lfloor (1-r)n \rfloor -2\)).


The matrix \(\mathbf {E}\mathcal L_{uu}\) can be written as \(I-M\), where \(M = D^{-1/2}AD^{-1/2}\) has a block form like in Proposition 2, with coefficients \(a = \dfrac{p_1}{d_1}\), \(b = c = \dfrac{q}{\sqrt{d_1 d_2}}\) and \(d = \dfrac{p_2}{d_2} \). Note that the blocks sizes are now \(\lfloor (1-r)n_i \rfloor \) and not \(n_i\). Under the symmetric assumption, we have \(d_1 = d_2 = \dfrac{n}{2}(p+q)\).

Moreover, \(\lambda _M\) is an eigenvalue of M if and only if \(1 - \lambda _M\) is eigenvalue of \(\mathbf {E}\mathcal L_{uu}\). Using the notations of Proposition 2, we have \(\varDelta = 4 (1-r)^2 \dfrac{q^2}{(p+q)^2}\), and the two non-zero eigenvalues of M are given by:

$$\begin{aligned} \lambda _\pm&= \dfrac{1}{2}\Big ( 2 (1-r) \dfrac{p}{p+q} \pm 2 (1-r) \dfrac{q}{p+q} \Big ) \\&= 1 - r \dfrac{p \pm q}{p+q}. \end{aligned}$$

   \(\square \)

B Spectral Norm of an Extracted Matrix

Proposition 4

Let A be a matrix and B an extracted matrix (non necessarily squared: we can remove rows and columns with different indices, and potentially more rows than columns, or vice versa) from A, then: \( ||B||_2 \le ||A||_2 \).


For two subsets I and J of \(\{1,\dots ,n\}\), let \(B = A_{IJ}\) the matrix obtained from A by keeping only the rows (resp. columns) in I (resp. in J). Then \(B = M_1 A M_2\) where \(M_1\) and \(M_2\) are two appropriately chosen permutation matrices. Thus their spectral norm is equal to one, and the result \(||B||_2 \le ||A||_2\) follows from the inequality \( ||B||_2 \le ||M_1||_2 ||A||_2 ||M_2||_2\).   \(\square \)

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Avrachenkov, K., Dreveton, M. (2019). Almost Exact Recovery in Label Spreading. In: Avrachenkov, K., Prałat, P., Ye, N. (eds) Algorithms and Models for the Web Graph. WAW 2019. Lecture Notes in Computer Science(), vol 11631. Springer, Cham.

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