## Abstract

We consider the problem of identifying underlying community-like structures in graphs. Toward this end, we study the stochastic block model (SBM) on *k*-clusters: a random model on *n* = *km* vertices, partitioned in *k* equal sized clusters, with edges sampled independently across clusters with probability *q* and within clusters with probability *p*, *p* > *q*. The goal is to recover the initial “hidden” partition of [*n*]. We study semidefinite programming (SDP)-based algorithms in this context. In the regime \(p = \frac {\alpha \log (m)}{m}\) and \(q = \frac {\beta \log (m)}{m}\), we show that a certain natural SDP-based algorithm solves the problem of *exact recovery* in the *k*-community SBM, with high probability, whenever \(\sqrt {\alpha } - \sqrt {\beta } > \sqrt {1}\), as long as \(k=o(\log n)\). This threshold is known to be the information theoretically optimal. We also study the case when \(k=\theta (\log (n))\). In this case however, we achieve recovery guarantees that no longer match the optimal condition \(\sqrt {\alpha } - \sqrt {\beta } > \sqrt {1}\), thus leaving achieving optimality for this range an open question.

### Keywords

- Graph partitioning
- Random models
- Stochastic block model
- Semidefinite programming
- Dual certificate

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## Notes

- 1.
Indeed by definition any vector \(y \in \mathbb {R}_{n | k} \oplus \mathbb {1}\) can be written as \(x + \delta \frac {\mathbb {1}}{\sqrt {n}}\) for some

*δ*and \(x \in \mathbb {R}_{n | k}\). For the purpose of proving positive definiteness, we can always divide by any positive number and can therefore consider \(\frac {y}{\|x\|}\). Also note that we can consider*y*or −*y*equivalently and hence can consider the case when*δ*> 0.

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## Acknowledgements

Most of the work presented in this paper was conducted while ASB was at Princeton University and partly conducted while ASB was at the Massachusetts Institute of Technology. ASB acknowledges support from AFOSR Grant No. FA9550-12-1-0317, NSF DMS-1317308, NSF DMS-1712730, and NSF DMS-1719545.

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## 1 Appendix

**Forms of Chernoff Bounds and Hoeffding Bounds Used in the Arguments**

### Theorem 7 (Chernoff)

*Suppose X*
_{1}…*X*
_{
n
} *be independent random variables taking values in* {0, 1}*. Let X denote their sum and let* \(\mu = \mathbb {E}[X]\) *be its expectation. Then for any δ *> 0 *it holds that*

*A simplified form of the above bound is the following formula (for δ *≤ 1)

### Theorem 8 (Bernstein)

*Suppose X*
_{1}…*X*
_{
n
} *be independent random variables taking values in* [−*M*, *M*]*. Let X denote their sum and let* \(\mu = \mathbb {E}[X]\) *be its expectation, then*

### Corollary 1

*Suppose X*
_{1}…*X*
_{
n
} *are i.i.d Bernoulli variables with parameter p. Let σ *=* σ*(*X*
_{
i
}) =* p*(1 −* p*)*; then we have that for any r *≥ 0

### Proof

We have that *nσ*
^{2} = *np*(1 − *p*) and *M* = 1. We can now choose \(t = \alpha \sigma \sqrt {n\log (r)} + \alpha \log (r)\). This implies that \(\frac {n\sigma ^2 + t/3}{t^2} \leq \frac {1}{\log (r)}\left (1/\alpha ^2 + 1/3\alpha \right ) \leq \frac {2}{\alpha \log (r)} \) which implies from Theorem 8 that \(\mathbb {P}\left (X > \mu + \alpha \sigma \sqrt {n\log (r)}+ \alpha \log (r)\right ) \leq e^{-\frac {\alpha \log (r)}{4}}.\) □

### Theorem 9 (Hoeffding)

*Let X*
_{1}…*X*
_{
n
} *be independent random variables. Assume that the X*
_{
i
} *are bounded in the interval* [*a*
_{
i
}, *b*
_{
i
}]*. Define the empirical mean of these variables as*

*then*

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Agarwal, N., Bandeira, A.S., Koiliaris, K., Kolla, A. (2017). Multisection in the Stochastic Block Model Using Semidefinite Programming. In: Boche, H., Caire, G., Calderbank, R., März, M., Kutyniok, G., Mathar, R. (eds) Compressed Sensing and its Applications. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-69802-1_4

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