1. Statistical Physics Lab, Department of Physics, University of SeoulMesoscale Structures of NetworksSang Hoon LeeDepartment of Energy Science, Sungkyunkwan Universityhttp://sites.google.com/site/lshlj82SHL, M. Cucuringu, and M. A. Porter, Density-based and transport-based core-periphery structures in networks, Phys. Rev. E 89, 032810 (2014);SHL, M. D. Fricker, and M. A. Porter, Mesoscale Analyses of Fungal Networks, e-print arXiv:1406.5855;M. Cucuringu, M. P. Rombach, SHL, and M. A. Porter, Detection of Core-Periphery Structure in Networks Using Spectral Methods andGeodesic Paths, e-print arXiv:1410.6572;SHL, M. Farazmand, G. Haller, and M. A. Porter, Finding Lagrangian Coherent Structures Using Community Detection, in preparation.

2. Outline core vs periphery structure in networks density-based vs transport-based coreness measures application to various real networks related to nestedess in ecological networks? community detection in networks mesoscopic response function (MRF) based taxonomy fungal networks (nutrient/energy transport) multilayer community detection Lagrangian coherent structures (ocean flow) political cosponsorship networks summary and outlook 3. mesoscale structures of a network in terms of transport1234567 4. mesoscale structures of a network in terms of transport1234567 5. mesoscale structures of a network in terms of transport1234567 6. mesoscale structures of a network in terms of transport123two modes or modules(or communities)4567R. Lambiotte, J.-C. Delvenne, and M. Barahona, arXiv:0812.1770;M. Rosvall and C. T. Bergstrom, PNAS 104, 7327 (2007); PNAS 105, 1118 (2008). 7. Community structure in networks adjacency matrixp1=0.5, p2=0.05, p3=0.5; pS=0, dS=00 20 40 60 80 1000102030405060708090100nz = 2730 8. Community structure in networks adjacency matrixp1=0.5, p2=0.05, p3=0.5; pS=0, dS=00 20 40 60 80 1000102030405060708090100nz = 2730modularity (the objective function to be maximized)Q =12XijWij