DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks

• Published in: Expert systems with applications 2020-11, Vol.159, p.113640, Article 113640
• Main Authors: Farhangi Maleki, Erfan, Ghadiri, Nasser, Lotfi Shahreza, Maryam, Maleki, Zeinab
• Format: Article
• Language: English
• Subjects: Algorithms; Apache Giraph; Biological effects; Complex networks; Distributed graph processing; Drug repositioning; Heterogeneous label propagation; Networks; Propagation; Semi-supervised learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113640

•We propose two distributed label propagation algorithms for heterogeneous networks.•The scalability of algorithms is measured on a heterogeneous drug-related network.•We evaluate the effectiveness of the algorithms for “drug repositioning".•The runtime of the algorithms is dramatically decreased.•Experiments indicate the high accuracy of our proposed algorithms. 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. 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. 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.