BROADCASTS.com

b'In this episode I explain how a community detection algorithm known as Markov clustering can be constructed by combining simple concepts like random walks, graphs, similarity matrix. Moreover, I highlight how one can build a similarity graph and then run a community detection algorithm on such graph to find clusters in tabular data.\\nYou can find a simple hands-on code snippet to play with on the Amethix Blog\\xa0\\nEnjoy the show!\\xa0\\n\\xa0\\nReferences\\n[1] S. Fortunato, \\u201cCommunity detection in graphs\\u201d, Physics Reports, volume 486, issues 3-5, pages 75-174, February 2010.\\n[2] Z. Yang, et al., \\u201cA Comparative Analysis of Community Detection Algorithms on Artificial Networks\\u201d, Scientific Reports volume 6, Article number: 30750 (2016)\\n[3] S. Dongen, \\u201cA cluster algorithm for graphs\\u201d, Technical Report, CWI (Centre for Mathematics and Computer Science) Amsterdam, The Netherlands, 2000.\\n[4] A. J. Enright, et al., \\u201cAn efficient algorithm for large-scale detection of protein families\\u201d, Nucleic Acids Research, volume 30, issue 7, pages 1575-1584, 2002.'