In this article we consider the spectrum of a Laplacian matrix, also known as the Markov matrix, under the independence assumption. We assume that the entries have a variance profile. Motivated by recent works on generalized Wigner matrices we assume that variance profile gives rise to a sequence of graphons. Under the assumption that these graphons converge, we show that the limiting spectral distribution of the scaled Laplacian converge. We give an expression for the moments of the limiting measure in terms of graph homomorphisms. In some special cases we identify the limit explicitly. We also study the spectral norm and derive the order of the maximum eigenvalue. We show that our results cover Laplacian of various random graphs include inhomogeneous Erdős- R\' enyi random graph, sparse W-random graphs, stochastic block matrices and constrained random graphs.