The emerging field of graph signal processing requires a solid design of downsampling operation for graph signals to extend pattern recognition, machine learning and signal processing techniques into the graph setting. The state-of-the-art downsampling method is constructed upon the maximum spanning trees of the graphs. However, under the framework of this method, unbalanced downsampling often occurs for signals defined on densely connected unweighted graphs, such as social network data. The unbalance also significantly reduces the maximal downsampling level, making it smaller than the level we expect. In applications, the maximal level must be estimated to ensure that it is larger than the expected level; meanwhile, the unbalance has to be reduced, if it occurs. In this paper, we propose a novel method to jointly estimate the maximal level and reduce the downsampling unbalance. This method also offers an estimation of the possibility of unbalanced downsampling. If a graph signal is classified to be with high unbalance possibility, the maximum spanning tree will be updated to generate a balanced downsampling. The simulation results on synthesis and real world data support the theoretical analysis.