Complex Networks Results of the 1st International Workshop on Complex Networks (CompleNet 2009)
We live in a networked world. People are getting more and more interconnected through the new information and communication technologies, like mobile phones and the Internet. The function of cells can be understood via networks of interacting proteins. Ecosystems can be described through networks of...
Other Authors: | , , , |
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Format: | eBook |
Language: | English |
Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2009, 2009
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Edition: | 1st ed. 2009 |
Series: | Studies in Computational Intelligence
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Subjects: | |
Online Access: | |
Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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