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Advanced Community Detection in Networks: Techniques, Applications, and Theoretical Insights

This course material from CS8803 Network Science covers various aspects of community detection in networks, including weighted and directed networks, overlapping and dynamic communities. Key methods such as modularity maximization and node classification based on connectivity are discussed. Applications span social, biological, brain, and climate networks, highlighting the importance of community structures. The materials include figures and videos used in class and provide insights into properties of real-world networks without detailed explanations.

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Advanced Community Detection in Networks: Techniques, Applications, and Theoretical Insights

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  1. CS8803-NSNetwork ScienceFall 2013 Instructor: Constantine Dovrolis constantine@gatech.edu http://www.cc.gatech.edu/~dovrolis/Courses/NetSci/

  2. Disclaimers The following slides include only the figures or videos that we use in class; they do not include detailed explanations, derivations or descriptionscovered in class. Many of the following figures are copied from open sources at the Web. I do not claim any intellectual property for the following material.

  3. Outline • Variations of the community detection problem • Weighted and/or directed networks • Overlapping communities • Dynamic communities • Community detection based on info theory • Properties of real-world network communities • Applications of community detection • In social networks • In biological networks • In brain networks • In climate networks

  4. In addition to its application domain, this paper covers: • Modularity maximization using Simulated Annealing • Classification of nodes (“the role of each node”) based on its connectivity within a community and across communities • Within-module degree • Participation coefficient • Which nodes are more conserved by evolution?

  5. http://memshub.org/resources/200/watch?resid=201

  6. Modularity for weighted, directed networks

  7. http://www.cise.ufl.edu/~tdinh/

  8. http://www.nature.com/nature/journal/v446/n7136/fig_tab/nature05670_F1.htmlhttp://www.nature.com/nature/journal/v446/n7136/fig_tab/nature05670_F1.html

  9. Ground-truth in network communities?

  10. Power method: How to compute the fraction of time spent at each node by a “random walker”?(suppose the network is undirected, for now) http://www.biomedcentral.com/1471-2105/7/71/figure/F8?highres=y

  11. Huffman encoding

  12. For directed networks:

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