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hi i'm melanie kimbers and i'm going to talk to you about massively parallel correlation clustering on bounded arboristic graphs this is a joint work with davinci havoc and yarawito the massively parallel computation model is a model in which we consider a large input graph the goal is to solve a problem on this graph using a collection of machines working in parallel the main restriction is that machines have a limited local memory s since we are working in the sublinear regime this memory is going to be sublinear in the size of the input in the number of nodes so n to the epsilon where epsilon is an arbitrary constant between 0 and 1. the global memory has to be larger than the input size in order to solve the problem but we want it to be as small as possible in our paper we use o of n to the 1 plus delta global memory and even reduce it to o of m log n by sacrificing some time efficiency when we run an npc algorithm the input graph is partitioned among the machines t

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