Partial Restreaming Approach for Massive Graph Partitioning

Graph partitioning is a challenging and highly important problem when performing computation tasks over large distributed graphs, the reason is that a good partitioning leads to faster computations. In this work, we introduce the partial rest reaming partitioning which is a hybrid streaming model allowing only several portions of the graph to be rest reamed while the rest is to be partitioned on a single pass of the data stream. We show that our method yields partitions of similar quality than those provided by methods rest reaming the whole graph (e.g. ReLDG, ReFENNEL), while incurring lower cost in running time and memory since only several portions of the graph will be rest reamed.