Network motifs are widely used to uncover structural design principles of complex networks. Current sequential network motif discovery algorithms become inefficient as motif size grows, thus parallelization methods have been proposed in the literature. In this study, we use star contraction algorithm to partition complex networks efficiently for parallel discovery of network motifs. We propose two new heuristics to make star contraction more suitable for partitioning of complex networks. The effectiveness of our partitioning strategies is verified using the ESU algorithm for subgraph counting. We also propose a ghost vertices detection algorithm to ensure that all the motifs located in multiple parts are exactly found. We implement our method using MPI libraries and tested on real-life complex networks of different domains. We compared speedups of star contraction algorithm with speedups of other graph partitioning algorithms. Our algorithm obtained better speedups than those of other partitioning algorithms for most cases. Our algorithm provides significant speedups when compared to sequential ESU algorithm allowing discovery of larger network motifs.

网络主题被广泛用于揭示复杂网络的结构设计原理。随着主题大小的增长，当前的顺序网络主题发现算法变得效率低下，因此文献中提出了并行化方法。在这项研究中，我们使用星形压缩算法对复杂网络进行有效划分，以并行发现网络主题。我们提出了两种新的启发式方法，以使星形收缩更适合于复杂网络的分区。我们使用ESU算法进行子图计数，验证了我们的分区策略的有效性。我们还提出了重影顶点检测算法，以确保准确找到位于多个部分的所有图案。我们使用MPI库实施我们的方法，并在不同域的实际复杂网络上进行了测试。我们将星形收缩算法的提速与其他图分区算法的提速进行了比较。在大多数情况下，我们的算法获得了比其他分区算法更好的加速效果。与顺序ESU算法相比，我们的算法可显着提高速度，从而可以发现更大的网络图案。