Near-optimal edge partitioning via intersecting families
Alexander Yakunin, Andrey Kupavskii, Alexander Sushin, Stanislav Moiseev

TL;DR
This paper introduces new efficient algorithms for edge partitioning in graphs that minimize vertex replication while balancing part sizes, achieving near-optimal bounds especially when the number of parts grows slowly.
Contribution
It develops a novel class of algorithms based on intersecting families that are asymptotically worst-case optimal for minimizing replication factor in edge partitioning.
Findings
Algorithms guarantee asymptotically optimal upper bounds on replication factor.
Optimal replication factor for growing number of parts is approximately . .
Algorithms are efficient in LOCAL, CONGEST models, and streaming frameworks.
Abstract
We study the problem of edge partitioning, where the goal is to partition the edge set of a graph into several parts. The replication factor of a vertex is the number of parts that contain edges incident to . The goal is to minimize the average replication factor of the vertices while keeping the sizes of the parts nearly equal. We study the regime where the number of parts is significantly smaller than the size of the graph. To this end, we introduce a new class of edge partitioning algorithms. These algorithms guarantee asymptotically worst-case-optimal upper bounds on the replication factor for any constant number of parts , and when grows slowly with the number of vertices. In particular, we show that the optimal replication factor for growing is . The algorithms are computationally efficient, including in the LOCAL and CONGEST models, and can…
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