A Balanced Partitioning Method for Big Graphs via Coarsen-Partition-Refining Steps With Preserving Atomic Subgraphs

Atomic subgraphs are inherent and functionally meaningful structures in real-world graphs, capturing cohesive units such as social communities, molecular functional groups, or neural circuits. Preserving these atomic subgraphs during graph partitioning is crucial for maintaining semantic integrity, improving algorithmic interpretability, and reducing communication overhead in parallel processing. However, traditional partitioning methods often overlook this structural prior, leading to fragmentation of such subgraphs and degradation in downstream analytical quality. In this work, we propose a novel balanced graph partitioning approach that explicitly preserves atomic subgraphs through a coarsen-partition-refine framework. In the coarsening phase, smaller subgraphs are merged into a larger one based on the maximum edge-to-vertex weight ratio between subgraphs. In the partitioning phase, a spectral k-way method divides the coarsened graph into k balanced blocks. In the refinement phase, boundary subgraphs are exchanged between target blocks via designed rules, reducing cut-edge weights and ultimately yielding higher-quality balanced partitions. We evaluate our method on real-world and synthetic datasets by generating graphs with diverse subgraph distributions. The experimental results demonstrate the feasibility and effectiveness of our method.