Background and objective

Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required.

Methods

In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network.

Results

We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy.

中文翻译：

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DHLP 1＆2：异类药物相关网络上基于Giraph的分布式标签传播算法

背景和目标

异构复杂网络是由不同类型的节点和边组成的大型图。从这些网络中提取知识非常复杂。而且，这些网络的规模正在稳步增加。因此，需要可扩展的方法。

方法

本文介绍了两种异构网络的分布式标签传播算法，即DHLP-1和DHLP-2。生物网络是异构复杂网络的一种类型。作为案例研究，我们在由药物，疾病和目标组成的生物网络上测量了我们提出的DHLP-1和DHLP-2算法的效率。我们在此网络中研究的主题是药物重新定位，但我们的算法可以用作除生物网络以外的异构网络的通用方法。

结果

我们将提出的算法与类似的非分布式版本MINProp和Heter-LP进行了比较。实验揭示了算法在运行时间和准确性方面的良好性能。