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Complex Networks: Results of the 1st International Workshop on Complex Networks

Complex Networks: Results of the 1st International Workshop on Complex Networks (Complenet 2009) (Hardcover, 2009)

(Results of the 2009 International Workshop on Complex Networks (CompleNet 2009))

Giuseppe Mangioni, Santo Fortunato (엮은이)
Springer-Verlag New York Inc
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Complex Networks: Results of the 1st International Workshop on Complex Networks
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책 정보

· 제목 : Complex Networks: Results of the 1st International Workshop on Complex Networks (Complenet 2009) (Hardcover, 2009) (Results of the 2009 International Workshop on Complex Networks (CompleNet 2009))
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 응용수학
· ISBN : 9783642012051
· 쪽수 : 225쪽
· 출판일 : 2009-08-11

목차

Session 1: Analysis of Real Networks.- Dynamics and Evolution of the International Trade Network.- Small World Behavior of the Planetary Active Volcanoes Network: Preliminary Results.- Correlation Patterns in Gene Expressions along the Cell Cycle of Yeast.- Session 2: Community Structure.- Detecting and Characterizing the Modular Structure of the Yeast Transcription Network.- Finding Overlapping Communities Using Disjoint Community Detection Algorithms.- Discovering Community Structure on Large Networks Using a Grid Computing Environment.- Finding Community Structure Based on Subgraph Similarity.- Session 3: Network Modeling.- Structural Trends in Network Ensembles.- Generalized Attachment Models for the Genesis of Graphs with High Clustering Coefficient.- Modeling Highway Networks with Path-Geographical Transformations.- Session 4: Network Dynamics.- Simplicial Complex of Opinions on Scale-Free Networks.- An Axiomatic Foundation for Epidemics on Complex Networks.- Analytical Approach to Bond Percolation on Clustered Networks.- Session 5: Applications.- Order-Wise Correlation Dynamics in Text Data.- Using Time Dependent Link Reduction to Improve the Efficiency of Topic Prediction in Co-Authorship Graphs.- Fast Similarity Search in Small-World Networks.- Detection of Packet Traffic Anomalous Behaviour via Information Entropy.- Identification of Social Tension in Organizational Networks.

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