LEADER 06198nam 22006615 450 001 9910299691003321 005 20200630035553.0 010 $a3-319-16112-1 024 7 $a10.1007/978-3-319-16112-9 035 $a(CKB)3710000000379625 035 $a(SSID)ssj0001465418 035 $a(PQKBManifestationID)11872618 035 $a(PQKBTitleCode)TC0001465418 035 $a(PQKBWorkID)11477913 035 $a(PQKB)10406866 035 $a(DE-He213)978-3-319-16112-9 035 $a(MiAaPQ)EBC6298994 035 $a(MiAaPQ)EBC5587730 035 $a(Au-PeEL)EBL5587730 035 $a(OCoLC)905220085 035 $a(PPN)184895367 035 $a(EXLCZ)993710000000379625 100 $a20150314d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aComplex Networks VI $eProceedings of the 6th Workshop on Complex Networks CompleNet 2015 /$fedited by Giuseppe Mangioni, Filippo Simini, Stephen Miles Uzzo, Dashun Wang 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (X, 232 p. 74 illus., 52 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v597 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-16111-3 327 $aA Flexible Fitness Function for Community Detection in Complex Networks -- Finding network motifs using MCMC sampling -- Analysis of the Robustness of Degree Centrality against Random Errors in Graphs -- A Model for Ambiguation and an Algorithm for Disambiguation in Social Networks -- Measuring the Generalized Friendship Paradox in Networks with Quality-dependent Connectivity -- Expected Nodes: a quality function for the detection of link communities -- Core-Periphery Models for Graphs Based on their -Hyperbolicity: An Example Using Biological Networks -- Fast Optimization of Hamiltonian for Constrained Community Detection -- Selecting Seed Nodes for Influence Maximization in Dynamic Networks -- Neighbourhood Distinctiveness: an initial study -- An Efficient Estimation of a Node?s Between ness -- Sentiment Classification Analysis of Chinese Microblog Network -- Techniques for Brain Functional Connectivity Analysis from High Resolution Imaging -- A Two-Parameter Method to Characterize the Network Reliability for Diffusive Processes -- Analysis of the Effects of Communication Delay in the Distributed Global Connectivity Maintenance of a Multi-Robot System -- Inter-Layer Degree Correlations in Heterogeneously Growing Multiplex Networks -- Dynamics of Conflicting Beliefs in Social Networks -- Building Mini-Categories in Product Networks -- Categorical Framework for Complex Organizational Networks: Understanding the Effects of Types, Size, Layers, Dynamics and Dimensions -- Studying Reciprocity and Communication Probability Ratio in Weighted Phone Call Ego Networks -- NetSci High: Bringing Network Science Research to High Schools -- Terrorism Dynamics on Complex Networks: Group Polarization vs Social Integration -- From Criminal Spheres of Familiarity to Crime Networks -- Communication Probability Ratio in Weighted Phone Call Ego Networks -- NetSci High: Bringing Network Science Research to High Schools -- Terrorism Dynamics on Complex Networks: Group Polarization vs Social Integration -- From Criminal Spheres of Familiarity to Crime Networks -- Communication Probability Ratio in Weighted Phone Call Ego Networks -- NetSci High: Bringing Network Science Research to High Schools -- Terrorism Dynamics on Complex Networks: Group Polarization vs Social Integration -- From Criminal Spheres of Familiarity to Crime Networks. 330 $aElucidating the spatial and temporal dynamics of how things connect has become one of the most important areas of research in the 21st century. Network science now pervades nearly every science domain, resulting in new discoveries in a host of dynamic social and natural systems, including: how neurons connect and communicate in the brain, how information percolates within and among social networks, the evolution of science research through co-authorship networks, the spread of epidemics, and many other complex phenomena. Over the past decade, advances in computational power have put the tools of network analysis in the hands of increasing numbers of scientists, enabling more explorations of our world than ever before possible. Information science, social sciences, systems biology, ecosystems ecology, neuroscience and physics all benefit from this movement, which combines graph theory with data sciences to develop and validate theories about the world around us. This book brings together cutting-edge research from the network science field and includes diverse and interdisciplinary topics such as: modeling the structure of urban systems, behavior in social networks, education and learning, data network architecture, structure and dynamics of organizations, crime and terrorism, as well as network topology, modularity and community detection. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v597 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a004.6 702 $aMangioni$b Giuseppe$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSimini$b Filippo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aUzzo$b Stephen Miles$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Dashun$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299691003321 996 $aComplex Networks VI$91412329 997 $aUNINA