IsoRankN: We describe IsoRankN (IsoRank-Nibble), a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error-tolerant and computationally efficient.

References in zbMATH (referenced in 11 articles )

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  1. Nassar, Huda; Kollias, Georgios; Grama, Ananth; Gleich, David F.: Scalable algorithms for multiple network alignment (2021)
  2. Chen, Shuo; Bowman, F. DuBois; Xing, Yishi: Detecting and testing altered brain connectivity networks with k-partite network topology (2020)
  3. Chung, Fan; Simpson, Olivia: Computing heat kernel PageRank and a local clustering algorithm (2018)
  4. Malmi, Eric; Chawla, Sanjay; Gionis, Aristides: Lagrangian relaxations for multiple network alignment (2017)
  5. Amir-Ghiasvand, Farid; Nowzari-Dalini, Abbas; Momenzadeh, Vida: \textitPin-Align: a new dynamic programming approach to align protein-protein interaction networks (2014)
  6. Daskin, Anmer; Grama, Ananth; Kais, Sabre: Multiple network alignment on quantum computers (2014)
  7. Ibragimov, Rashid; Malek, Maximilian; Guo, Jiong; Baumbach, Jan: GEDEVO: an evolutionary graph edit distance algorithm for biological network alignment (2013)
  8. Li, Angsheng; Peng, Pan: Detecting and characterizing small dense bipartite-like subgraphs by the bipartiteness ratio measure (2013)
  9. Liu, Sijia; Matzavinos, Anastasios; Sethuraman, Sunder: Random walk distances in data clustering and applications (2013)
  10. Peng, Pan: The small community phenomenon in networks: models, algorithms and applications (2012)
  11. Liao, Chung-Shou; Lu, Kanghao; Baym, Michael; Singh, Rohit; Berger, Bonnie: Isorankn: spectral methods for global alignment of multiple protein networks (2009) ioport