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One common problem that spans several diverse applications is the management and derivation of knowledge from huge amounts of data, especially in scenarios involving human and social activities. In many practical situations, a real-life dataset can be represented as a large network (graph) – a structure that can be easily understood and visualized. Furthermore, special structures of graphs, when viewed in the context of a given application, provide insights into the internal structure and patterns of the data. Among the many examples of datasets that can be represented as graphs are the Web graph derived from the World Wide Web, the Call graph arising in telecommunications traffic data, and metabolic networks arising in biology. Of particular interest are social networks, in which vertices represent people or groups of people.
Although the concept of a network roots back to the ancient Greek philosopher Pythagoras in his theory of cosmos (κὁσμος), the mathematical principles of networks were first developed in the last century. The first book in networks appeared in 1936 (D. König: Theory of Finite and Infinite Graphs). Since then, there has been a huge explosion of research regarding theoretical tools and algorithms in the analysis of networks.
One of the most exciting moments came at the dawn of the new Millennium, in 1999 with the discovery of new types of graphs, called complex networks. Examples of such well-known classes of complex networks are scale-free networks and smallworld networks. These classes of networks are characterized by specific structural features such as the power-law vertex degree distribution (scale-free networks) and for the short path lengths, small diameter, and high clustering (small-world networks). Moreover, several other measures and features have been discovered, and are recently the focus of active research, that related to the structural properties of complex networks. A new area of complex networks has been rapidly developing, spanning several disciplines such as mathematics, physics, computer science, social science, biology, and telecommunications.
In our two volume handbook, an attempt was made to present a wide spectrum of recent developments with emphasis in both theory and applications on complex networks. The first volume focuses on basic theory and properties of complex networks, on their structure and dynamics, and optimization algorithmic approaches. The last part of the volume concentrates on some feature applications. The second volume, this volume, deals with the emerging issues on communication networks and social networks. It covers material on vulnerability and robustness of complex networks. The second part is dedicated to complex communication networks, discussing several critical problems such as traffic activity graph analysis, throughput optimization, and traffic optimization. The last part of this volume focuses on recent research topics on online social networks such as security and privacy, social aware solutions, and social based routing algorithms.
Basic Theory and Properties
Optimization in Designing Complex Communication Networks
Fitness-Based Generative Models for Power-Law Networks
Double Pareto Lognormal Distributions in Complex Networks
Laplacian Spectra and Synchronization Processes on Complex Networks
Growing Networks Driven by the Evolutionary Prisoner’s Dilemma Game
Structure and Dynamics of Complex Networks
Defining and Discovering Communities in Social Networks
Modularity Maximization and Tree Clustering: Novel Ways to Determine Effective Geographic Borders
Emergence and Structure of Cybercommunities
k-Core Organization in Complex Networks
Complex Networks Optimization Techniques
Hardness Complexity of Optimal Substructure Problems on Power-Law Graphs
Path Problems in Complex Networks
Optimized Design of Large-Scale Social Welfare Supporting Systems on Complex Networks
Optimal Flows in Dynamic Networks and Algorithms for their Finding
Some Distributed Approaches to the Service Facility Location Problem in Dynamic and Complex Networks
Applications
Modeling Epidemic Spreading in Complex Networks: Concurrency and Traffic
Theory of Citing
TTLed Random Walks for Collaborative Monitoring in Mobile and Social Networks