A Novel Network Aligner for the Analysis of Multiple Protein-protein Interaction Networks

Jing Chen1, 2 and Jia Huang1

  1. School of Artificial Intelligence and Computer Science
    Jiangnan University Wuxi, China
  2. Jiangsu Provincial Engineering Laboratory of Pattern
    Recognition and Computing Intelligence, Jiangnan University, Wuxi, China


The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the nodes is calculated according to the network structures. Second, the seed-and-extend framework is used to conduct an iterative search. In each iteration, a clustering method is combined to generate the alignment. Extensive experimental results show that ACCMNA outperformed the state-of-the-art algorithms in producing functionally consistent and topological conservation alignments within an acceptable running time.

Key words

graph data analysis, big data, protein-protein interaction network, network clustering, seed-and-extend strategy

Digital Object Identifier (DOI)


Publication information

Volume 18, Issue 4 (September 2021)
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Chen, J., Huang, J.: A Novel Network Aligner for the Analysis of Multiple Protein-protein Interaction Networks. Computer Science and Information Systems, Vol. 18, No. 4, 1427–1444. (2021), https://doi.org/10.2298/CSIS200909030C