AI 中文总结
针对大规模有符号二分图平衡蝴蝶计数效率低的问题，提出基于MPI+TBB的分布式算法D-BBC，在15个数据集上较串行和分布式基线分别实现最高1321倍和23.58倍加速。
AI 中文摘要
平衡蝴蝶是分析有符号二分图的基本原语，并为研究高阶结构性质（如聚类系数和社区结构）提供了基础。尽管其重要性，现有方法主要依赖串行算法进行平衡蝴蝶计数，这在大规模图上变得低效。为解决这一限制，我们提出了一种基于混合MPI+TBB框架的分布式算法D-BBC，该算法利用MPI进行进程间通信，利用Intel TBB进行节点内并行。我们在15个真实世界数据集上对所提方法进行了实验评估。实验结果表明，在单节点分布式系统上，D-BBC相较于串行BB2K和多核M-BBC实现分别实现了平均1321倍和16.2倍的加速比。此外，在端到端执行时间方面，D-BBC相较于分布式基线S-Monarch实现了最大23.58倍的加速比。这些结果证明了所提出的分布式方法的效率及其在大规模二分图上实现高性能有符号模体分析的潜力。
英文摘要
The balanced butterfly is a fundamental primitive for analyzing signed bipartite graphs and provides a basis for studying higher-order structural properties, such as clustering coefficients and community structure. Despite its importance, existing approaches primarily rely on serial algorithms for balanced butterfly counting, which become inefficient on large-scale graphs. To address this limitation, we propose a distributed algorithm, D-BBC, based on a hybrid MPI+TBB framework that exploits MPI for inter-process communication and Intel TBB for intra-node parallelism. We conduct an experimental assessment of the proposed approach across 15 real-world datasets. Experimental results demonstrate that, on a single-node distributed system, D-BBC achieves average speedups of 1321x and 16.2x over the serial BB2K and multi-core M-BBC implementations, respectively. Furthermore, D-BBC achieves a maximum speedup of 23.58x over the distributed baseline S-Monarch in terms of end-to-end execution time. These results demonstrate the efficiency of the proposed distributed approach and its potential to enable high- performance signed motif analysis on large-scale bipartite graphs.
