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| 008 | 150314s2015 gw | s |||| 0|eng d | ||
| 020 | _a9783319161129 | ||
| 024 | 7 |
_a10.1007/978-3-319-16112-9 _2doi |
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| 040 |
_bspa _dES-MaUEC |
||
| 050 | 4 |
_aTK5105.5 _b2015 EB |
|
| 245 | 1 | 0 |
_aComplex Networks VI _bProceedings of the 6th Workshop on Complex Networks CompleNet 2015 _cedited by Giuseppe Mangioni, Filippo Simini, Stephen Miles Uzzo, Dashun Wang. |
| 264 | 1 |
_aCham _bSpringer International Publishing _c2015 |
|
| 300 | _a1 recurso en línea (X, 232 páginas 74 ilustraciones, 52 ilustraciones a color.) | ||
| 336 |
_2rdacontent _aTexto (visual) _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
||
| 338 |
_2rdacarrier _arecurso electrónico _bcr |
||
| 490 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v597 |
|
| 490 | 0 | _aEngineering (Springer-11647) | |
| 505 | 0 | _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. | |
| 520 | 3 | _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. | |
| 988 | _aEBSPRINGER_2018 | ||
| 650 | 7 |
_aRedes informáticas _vCongresos y asambleas _2embne _9141354 |
|
| 700 | 1 |
_aMangioni, Giuseppe. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/nb2015009604 _1http://viaf.org/viaf/9397147665847760670003/ |
|
| 700 | 1 |
_aSimini, Filippo. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/307342923/ |
|
| 700 | 1 |
_aUzzo, Stephen Miles. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aWang, Dashun. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 776 | 0 | 8 |
_iEdición impresa: _z9783319161136 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319161112 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319386508 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-16112-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b04/2019 _dz _eIG _zSI |
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