Graph encoding methods have been proven exceptionally useful in many classification tasks — from molecule toxicity prediction to social network recommendations. However, most of the existing methods are designed to work in a centralized environment that requires the whole graph to be kept in memory. Moreover, scaling them on very large networks remains a challenge. In this work, we propose a distributed and permutation invariant graph embedding method denoted as Distributed Graph Statistical Distance (DGSD) that extracts graph representation on independently distributed machines. DGSD finds nodes’ local proximity by considering only nodes’ degree, common neighbors and direct connectivity that allows it to run in the distributed environment. On the other hand, the linear space complexity of DGSD makes it suitable for processing large graphs. We show the scalability of DGSD on sufficiently large random and real-world networks and evaluate its performance on various bioinformatics and social networks with the implementation in a distributed computing environment.

图编码方法已被证明在许多分类任务中异常有用-从分子毒性预测到社交网络推荐。但是，大多数现有方法都设计为在集中式环境中工作，该环境要求将整个图形保存在内存中。而且，在非常大的网络上扩展它们仍然是一个挑战。在这项工作中，我们提出了一种分布和置换不变图嵌入方法，称为“分布图统计距离”（DGSD），可在独立分布的计算机上提取图形表示。DGSD通过仅考虑节点的程度，公共邻居和允许其在分布式环境中运行的直接连接来查找节点的本地邻近度。另一方面，DGSD的线性空间复杂度使其适合处理大型图形。我们展示了DGSD在足够大的随机网络和现实网络上的可伸缩性，并通过在分布式计算环境中的实现来评估其在各种生物信息学和社交网络上的性能。