LEADER 06345nam 22007455 450 001 9910300534203321 005 20200705040229.0 010 $a3-319-73198-X 024 7 $a10.1007/978-3-319-73198-8 035 $a(CKB)3840000000347925 035 $a(MiAaPQ)EBC5301817 035 $a(DE-He213)978-3-319-73198-8 035 $a(PPN)224639188 035 $a(EXLCZ)993840000000347925 100 $a20180215d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aComplex Networks IX $eProceedings of the 9th Conference on Complex Networks CompleNet 2018 /$fedited by Sean Cornelius, Kate Coronges, Bruno Gonçalves, Roberta Sinatra, Alessandro Vespignani 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (341 pages) 225 1 $aSpringer Proceedings in Complexity,$x2213-8684 311 $a3-319-73197-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aPart I: Theory of Complex Networks -- On the Eccentricity Function in Graphs -- Density Decompositions of Networks -- 188 Fast Streaming Small Graph Canonization -- Silhouette for the Evaluation of Community Structures in Multiplex Networks -- Jaccard Curvature: An Efficient Proxy for Ollivier-Ricci Curvature in Graphs -- Combinatorial Miller-Hagberg Algorithm for Randomization of Dense Networks -- Proposal of Strategic Link Addition for Improving the Robustness of Multiplex Networks -- Part II: Graph Embeddings -- Embedding-Centrality: Generic Centrality Computation Using Neural Networks -- Fast Sequence Based Embedding with Diffusion Graphs -- Semi?supervised Graph Embedding Approach to Dynamic Link Prediction -- Modularity Optimization as a Training Criterion for Graph Neural Networks -- Part II: Network Dynamics -- Outer synchronization for General Weighted Complex Dynamical Networks Considering Incomplete Measurements of Transmitted Information -- Diffusive Phenomena in Dynamic Networks: A Data-driven Study -- Fractal Analyses of Networks of Integrate-and-Fire Stochastic Spiking Neurons -- Part IV: Network Science Applications -- Cultivating Tipping Points: Network Science in Teaching -- Terrorist Network Analyzed with an Influence Spreading Model -- Author Attribution using Network Motifs -- Complex Networks Reveal a Glottochronological Classification of Natural Languages -- A Percolation-based Thresholding Method with Applications in Functional Connectivity Analysis -- Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams -- Router Level Topologies of Autonomous Systems -- Part V: Human Behavior and Social Networks -- Social Influence (Deep) Learning for Human Behavior Prediction -- Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation -- Are Crisis Platforms Supporting Citizen Participation? -- Dynamic Visualization of Citation Networks and Detection of Influential Node Addition -- A Trust-Based News Spreading Model -- Discovering Mobility Functional Areas: A Mobility Data Analysis Approach -- Estimating Peer Influence Effects Under Homphily: Randomized Treatments and Insights. 330 $aThis book aims to bring together researchers and practitioners working across domains and research disciplines to measure, model, and visualize complex networks. It collects the works presented at the 9th International Conference on Complex Networks (CompleNet) 2018 in Boston, MA in March, 2018. With roots in physical, information and social science, the study of complex networks provides a formal set of mathematical methods, computational tools and theories to describe prescribe and predict dynamics and behaviors of complex systems. Despite their diversity, whether the systems are made up of physical, technological, informational, or social networks, they share many common organizing principles and thus can be studied with similar approaches. This book provides a view of the state-of-the-art in this dynamic field and covers topics such as group decision-making, brain and cellular connectivity, network controllability and resiliency, online activism, recommendation systems, and cyber security. 410 0$aSpringer Proceedings in Complexity,$x2213-8684 606 $aPhysics 606 $aSocial sciences?Data processing 606 $aSocial sciences?Computer programs 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational complexity 606 $aApplications of Graph Theory and Complex Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/P33010 606 $aComputational Social Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/X34000 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022 615 0$aPhysics. 615 0$aSocial sciences?Data processing. 615 0$aSocial sciences?Computer programs. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aComputational complexity. 615 14$aApplications of Graph Theory and Complex Networks. 615 24$aComputational Social Sciences. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aComplexity. 676 $a004.6 702 $aCornelius$b Sean$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCoronges$b Kate$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGonçalves$b Bruno$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSinatra$b Roberta$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVespignani$b Alessandro$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300534203321 996 $aComplex Networks IX$91864477 997 $aUNINA