In his thesis (section 2.3.3) Belkin uses the heat equation to derive an approximation for $\mathcal{L}f$:
$$\mathcal{L}f(x_i)\approx \frac{1}{t}\Big(f(x_i)-\alpha \sum_{x_j, ||x_i-x_j||<\epsilon}e^{-\frac{||x_i-x_j||^2}{4t}}f(x_j)\Big)$$ where $$\alpha=\Big(\sum_{x_j, ||x_i-x_j||<\epsilon}e^{-\frac{||x_i-x_j||^2}{4t}}\Big)^{-1}$$.
However, I'm not sure how these considerations lead to this choice of weights for the weight matrix (which will be used to construct the Laplacian):
$$W_{ij} = \begin{cases} e^{-\frac{||x_i-x_j||^2}{4t}} & if\ ||x_i-x_j||<\epsilon \\ 0 & otherwise \end{cases}$$
A very vague idea of mine was that the factors $\alpha$ and $\frac{1}{t}$ don't change for a given $x_i$ so if one choses the weights like above the resulting discrete Laplacian would (let aside those two constants) converge to the continuous version.
Any ideas or tips what I'd have to read up to in order to get a better understanding?