Recently, a number of graph partitioning applications have emerged with additional requirements that the traditional graph partitioning model alone cannot e ectively handle. One such class of problems is those in which multiple objectives, each of which can be modeled as a sum of weights of the edges of a graph, must be simultaneously optimized. This class of problems can be solved utilizing a multi-objective graph partitioning algorithm. We present a new formulation of the multi-objective graph partitioning problem and describe an algorithm that computes partitionings with respect to this formulation. We explain how this algorithm provides the user with a ne-tuned control of the tradeo s among the objectives, results in predictable partitionings, and is able to handle both similar and dissimilar objectives. We show that this algorithm is better able to nd a good tradeo among the objectives than partitioning with respect to a single objective only. Finally, we show that by modifying t...