Balanced Graph Partitioning for Optimizing Big Data Workloads and Motif Computation

This paper introduces two novel graph partitioning problems motivated by optimizing big data computing applications: (1) workload-driven balanced graph partitioning to optimize the performance of specific workloads, and (2) motif-driven balanced graph partitioning to optimize the computation of graph motifs. The paper provides formal problem definitions, complexity analyses, and bi-criteria approximation algorithms with performance guarantees for these problems.