The baseband-up centralization architecture of radio access networks (C-RAN) has recently been proposed to support efficient cooperative communications and reduce deployment and operational costs. However, the massive fronthaul bandwidth required to aggregate baseband samples from remote radio heads (RRHs) to the central office incurs huge fronthauling cost, and existing baseband compression algorithms can hardly solve this issue. In this paper, we propose a graphbased framework to effectively reduce fronthauling cost through properly splitting and placing baseband processing functions in the network. Baseband transceiver structures are represented with directed graphs, in which nodes correspond to baseband functions, and edges to the information flows between functions. By mapping graph weighs to computational and fronthauling costs, we transform the problem of finding the optimum location to place some baseband functions into the problem of finding the optimum clustering scheme for graph nodes. We then solve this problem using a genetic algorithm with customized fitness function and mutation module. Simulation results show that proper splitting and placement schemes can significantly reduce fronthauling cost at the expense of increased computational cost. We also find that cooperative processing structures and stringent delay requirements will increase the possibility of centralized placement.