PPT-Finding Dense and Connected Subgraphs in Dual Networks

Yubao Wu Ruoming Jin Xiaofeng Zhu Xiang Zhang EECS CWRU CS Kent State U EPBI CWRU EECS CWRU Dual Biological N etworks a protein interaction network b genetic interaction network. McCurley Andrew Tomkins Abstract connection subgraph is a small ie viewable subgraph of a social network that best captures the relationship between two people We present a formal de64257nition of this problem and an ideal solution based on computa Government. Transforming a Community, a Country, the World. Rick Hutley. Vice President, Cisco Systems. Four Mega-Trends Changing our World. Developed countries’ labor forces substantially age. and decline in numbers. degree. Raphael Yuster. 2012. Problems concerning edge-disjoint subgraphs that share some specified property are extensively studied in graph . theory.. Many fundamental problems can be formulated in this . jA[Bj:Thatis,theprobabilitythatthesmallestelementofAandBisthesame,wheresmallestisdenedby thepermutation,isexactlythesimilarityofthetwosetsaccordingtotheJaccardcoecient.Usingthisobservation,wecomput Bicoloured. Graphs: Dually Connectedness, . Dual Separators, and Beyond. CAI . Leizhen. The Chinese . Univ. of Hong Kong. Joint work with YE . Junjie. 2. 3. 4. Corneilian. Graph. Charles. Mark. Lorna . Haenggi. et al. EE 360 : 19. th. February 2014. . Contents. SNR, SINR and geometry. Poisson Point Processes. Analysing interference and outage. Random Graph models. Continuum percolation and network models. AND K-MEDIAN PROBLEMS. K. Jain V. . Vazirani. Journal of . the . ACM, 2001.  . PRIMAL-DUAL APPROACH. We start by constructing a primal problem.. Networks:. . Models. , Measures and Optimal . Control. Published. in ICDM 2015. Chen Chen, . Jingrui. He, . Nadya. Bliss and . Hanghang. Tong. Q. 1: Multi-layered Networks Models(MULAN). A. Quintuple as the model:. and. Core-periphery . structure. By: Ralucca Gera, NPS. Why?. Mostly observed real networks have:. Heavy tail (. powerlaw. most probably, exponential). High clustering (high number of triangles especially in social networks, lower count otherwise). Algorithms. for . Analyzing Massive Graphs. Yubao. Wu. Case Western Reserve University. August 28, 2015. Graphs are Everywhere. Biological Networks. Social Networking Websites. Research Collaboration Network. this chapter you . will . learn-. W. hat . a . network . does. C. omponents . of a wired . network. To use . a . network. to share resources. Wired Networks. Networks. . come in . many sizes and vary widely . Bill . Molesworth. Vice President, Terrestrial Wireless Services. ORBCOMM Inc.. Intelligent Assets . Are Everywhere. Transportation . •. . Heavy Equipment . •. . Maritime. Industrial/Remote . Assets . Core-periphery structure. Excellence Through Knowledge. Prof. Ralucca Gera, rgera@nps.edu . Applied Mathematics Department, . Naval Postgraduate School. Learning Outcomes. Understand and contrast the different k-clique relaxation definitions:. Here in mesh network nodes are arranged as a q-dimensional lattice. The neighboring nodes are only allowed to communicate the data in one step i.e., each . PEi. is allowed to send the data to any one of PE(i+1) , PE (i-1), .