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 restreaming partitioning which is a hybrid streaming model allowing only several portions of the graph to be restreamed 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 restreaming the whole graph (e.g ReLDG, ReFENNEL) [1], while incurring lower cost in running time and memory since only several portions of the graph will be restreamed.