An automated method for finding molecular complexes in large protein interaction networks. Results: This paper describes a novel graph theoretic clustering algorithm, ”Molecular Complex Detection” (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion: Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from

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  1. Luo, Qi; Yu, Dongxiao; Li, Feng; Cheng, Xiuzheng; Cai, Zhipeng; Yu, Jiguo: Distributed core decomposition in probabilistic graphs (2021)
  2. Mahdavi Pajouh, Foad: Minimum cost edge blocker clique problem (2020)
  3. Walteros, Jose L.; Buchanan, Austin: Why is maximum clique often easy in practice? (2020)
  4. Zhou, Qing; Benlic, Una; Wu, Qinghua: An opposition-based memetic algorithm for the maximum quasi-clique problem (2020)
  5. Balister, Paul; Bollobás, Béla; Sahasrabudhe, Julian; Veremyev, Alexander: Dense subgraphs in random graphs (2019)
  6. Rasti, Saeid; Vogiatzis, Chrysafis: A survey of computational methods in protein-protein interaction networks (2019)
  7. Wang, Jie; Liang, Jiye; Zheng, Wenping; Zhao, Xingwang; Mu, Junfang: Protein complex detection algorithm based on multiple topological characteristics in PPI networks (2019)
  8. Yan, Ting; Jiang, Binyan; Fienberg, Stephen E.; Leng, Chenlei: Statistical inference in a directed network model with covariates (2019)
  9. Yu, Yang; Zheng, Zeyu: Protein complex identification based on weighted PPI network with multi-source information (2019)
  10. Kawase, Yasushi; Miyauchi, Atsushi: The densest subgraph problem with a convex/concave size function (2018)
  11. Liu, Wei; Ma, Liangyu; Jeon, Byeungwoo; Chen, Ling; Chen, Bolun: A network hierarchy-based method for functional module detection in protein-protein interaction networks (2018)
  12. Li, Wenlong; Yan, Ting; Abd Elgawad, Mohamed; Qin, Hong: Degree-based moment estimation for ordered networks (2017)
  13. Solymosi, David; Solymosi, Jozsef: Small cores in 3-uniform hypergraphs (2017)
  14. Wilson, James D.; Palowitch, John; Bhamidi, Shankar; Nobel, Andrew B.: Community extraction in multilayer networks with heterogeneous community structure (2017)
  15. Zhao, Jie; Lei, Xiujuan; Wu, Fang-Xiang: Predicting protein complexes in weighted dynamic PPI networks based on ICSC (2017)
  16. Pajouh, Foad Mahdavi; Balasundaram, Balabhaskar; Hicks, Illya V.: On the 2-club polytope of graphs (2016)
  17. Veremyev, Alexander; Prokopyev, Oleg A.; Butenko, Sergiy; Pasiliao, Eduardo L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs (2016)
  18. Yan, Ting; Leng, Chenlei; Zhu, Ji: Asymptotics in directed exponential random graph models with an increasing bi-degree sequence (2016)
  19. Yong, Zhang; Chen, Siyu; Qin, Hong; Yan, Ting: Directed weighted random graphs with an increasing bi-degree sequence (2016)
  20. Žurauskienė, Justina; Kirk, Paul D. W.; Stumpf, Michael P. H.: A graph theoretical approach to data fusion (2016)

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