GAP : Generalizable Approximate Graph Partitioning Framework
Abstract
Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions. Due to its combinatorial
nature, many approximate solutions have been developed. We propose GAP, a Generalizable Approximate Partitioning framework that takes a deep learning approach
to graph partitioning. We define a differentiable loss function that represents the
partitioning objective. Unlike baselines that redo the optimization per graph, GAP
is capable of generalization, allowing us to train models that produce performant
partitions at inference time, even on unseen graphs. Furthermore, because we learn
the representation of the graph while jointly optimizing for the partitioning loss
function, GAP can be easily tuned for a variety of graph structures. We evaluate the
performance of GAP on graphs of varying sizes and structures, including graphs
of widely used machine learning models (e.g., ResNet, VGG, and Inception-V3),
scale-free graphs, and random graphs.
nature, many approximate solutions have been developed. We propose GAP, a Generalizable Approximate Partitioning framework that takes a deep learning approach
to graph partitioning. We define a differentiable loss function that represents the
partitioning objective. Unlike baselines that redo the optimization per graph, GAP
is capable of generalization, allowing us to train models that produce performant
partitions at inference time, even on unseen graphs. Furthermore, because we learn
the representation of the graph while jointly optimizing for the partitioning loss
function, GAP can be easily tuned for a variety of graph structures. We evaluate the
performance of GAP on graphs of varying sizes and structures, including graphs
of widely used machine learning models (e.g., ResNet, VGG, and Inception-V3),
scale-free graphs, and random graphs.
Research Areas
