Complex Networks

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 taxonomic relationships between species. The network representation has proved to be a powerful tool to understand the structure and the dynamics of complex systems. Since the pioneering discovery of the scale-free property of the World Wide Web by Albert, Jeong and Barabási, the study of complex networks has become the leading discipline in complexity science. This volume is intended to bring to the attention of the scientific community recent advances in complex networks. It covers significant aspects of networks' structure and dynamics, both from the analytical and the empirical point of view. The works of this collection are contributed by a truly interdisciplinary community of scientists, from physicists to mathematicians, from computer scientists to engineers and economists.

Klappentext

Though the reductionist approachto biology and medicine has led to several imp- tant advances, further progresses with respect to the remaining challenges require integration of representation, characterization and modeling of the studied systems along a wide range of spatial and time scales. Such an approach, intrinsically - lated to systems biology, is poised to ultimately turning biology into a more precise and synthetic discipline, paving the way to extensive preventive and regenerative medicine [1], drug discovery [20] and treatment optimization [24]. A particularly appealing and effective approach to addressing the complexity of interactions inherent to the biological systems is provided by the new area of c- plex networks [34, 30, 8, 13, 12]. Basically, it is an extension of graph theory [10], focusing on the modeling, representation, characterization, analysis and simulation ofcomplexsystemsbyconsideringmanyelementsandtheirinterconnections.C- plex networks concepts and methods have been used to study disease [17], tr- scription networks [5, 6, 4], protein-protein networks [22, 36, 16, 39], metabolic networks [23] and anatomy [40].

Inhalt

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.