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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a spectral clustering algorithm for the sparse degree regime (degree=\theta(1)) of the stochastic blockmodel. The authors propose an alternative to the non-backtracking operator and point out that this has similar properties as the non-backtracking operator, which has been shown to be useful in the sparse regime. But the proposed data matrix is smaller than the non backtracking operator, and symmetric, therefore making eigenvalue computation easier and more accurate. The sparse regime is indeed the hardest in terms of showing concentration of empirical eigenvalues, or performance of a clustering algorithm in the stochastic blockmodel.