In this work, we study the problem of distributed sampling and interpolation for perfect reconstruction of graph signals. In particular, we explore and present a generalization of Papoulis' classic generalized sampling expansion (GSE) to graph signals. We consider a single-time instance of a graph signal from a space of bandlimited graph signals, appropriately defined via the graph Fourier transform associated to the graph. For such bandlimited graph signals, we first identify a sufficient condition for perfect reconstruction via distributed sampler/interpolator pairs, in the spirit of the Shannon-Nyquist criterion. When this perfect reconstruction criteria is satisfied by the individual sampler rates, we then propose a distributed sampler/interpolator architecture which is shown to be achievable for the underlying bandlimited space. The results represent a unique generalization of Papoulis' generalized sampling expansion (GSE) paradigm to graph signals. Interestingly, our results show that such achievable schemes-comprising several pairs of individual sampler/interpolator pairs- are such that every component sampler can be essentially perceived as a concatenation of a pre-sampling filtering operation followed by binary vertex-sampling. The corresponding interpolator is then obtained as a linear transformation which is completely dependent on the vertex-sampling operation but is independent of the pre-sampling filter.