Characterizing, describing, and extracting information from a network is by now one of the main goals of science, since the study of network currently draws the attention of several fields of research, as biology, economics, social science, computer science and so on. The main goal is to analyze networks in order to extract their emergent properties and to understand functionality of such complex systems. This work concerns the analysis of biological networks and the two main approaches are treated: the first based on the study of their topological structure, the second based on the dynamic properties of the system described by a network. Original methods are presented to extract information from a network through both static and dynamic approaches opening new perspectives in biological network analysis.
Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.
Biological networks encapsulate invaluable information about the roles of different entities and their interactions with each other. Analyzing these networks is essential to reveal evolutionary differences between different cells and organisms. An important type of analysis is the comparative analysis which aims at identifying functionally similar components of these networks. Analogous to sequence alignment which identifies sequence similarity, network alignment reveals similar connectivity patterns such as alternative paths and subnetworks. Additional to the comparative analysis, examining solely the topological structure led to discovery of the steady states and the modular organization that these networks exhibit. This book introduces (i) Alignment algorithms for metabolic networks that account for heterogeneous network elements, connected subnetwork mappings and the scalability problem; (ii) An algorithm that predicts functional similarity between reactions based on metabolic flux analysis; (iii) Efficient methods that identify steady states of Boolean regulatory networks; (iv) An algorithm that identifies dynamic modular structure of regulatory networks.
Graphs and networks are ubiquitous representations of computer, biological and social systems. In this book we study characteristic properties of networks that can help us to understand the systems they represent. Specifically, we study patterns of correlations between connected nodes in a network and provide mechanistic explanations for the origins of such correlations. We will show that computer, biological and social networks have some commonalities among them. On the other hand, they also have distinctive features, which are reflected in the patterns of correlations between connected nodes in the network. Finally, we will show these correlations can affect the behavior of different processes taking place on networks, such as the spreading of computer viruses, infectious diseases, or the association between genes and biological function.
Development of advanced techniques for biological network visualization is crucial for successful progress in the areas of systems-level biology and data-intensive bioinformatics. However, current techniques for biological network visualization fall short of expectations for representing extensive biological networks. In order to provide useful network visualization tools, new approaches have to be proposed and applied alongside with those most powerful features of current visualization systems. The resulting representation techniques have to be tested by applying to large-scale examples that would include metabolic, signaling and gene expression events.
Various intelligent techniques were used to solve the Shortest Path Problems in the Networks like Artificial Neural Networks (ANN), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), etc. With the recent up-gradation in wireless technology,mobile communication between various nodes has become possible. One of the most important characteristics in mobile wireless networks is the topology dynamics,the network topology keeps on changing due to energy conservation or node mobility. Therefore, the shortest path routing problem becomes a Dynamic Optimization Problem (DOP) in Mobile Networks. Here the use of various optimization algorithms to solve the dynamic shortest path problem in Mobile Networks and their comparisons are discussed.
IT IS generally believed by the research community that the introduction of complex network functions—such as routing—in the optical domain will allow a better network utilisation, lower cost and footprint, and a more efficiency in energy usage. The new optical components and sub-systems intended for dynamic optical networking introduce new kinds of physical layer impairments in the optical signal. Consequently, the aim of this book was to first identify and characterise the physical layer impairments of dynamic optical networks, and then digital signal processing techniques were developed to mitigate them. The initial focus of this work was the design and characterisation of digital optical receivers for dynamic core optical networks. Digital receiver techniques allow for complex algorithms to be implemented in the digital domain, which usually outperform their analogue counterparts in performance and flexibility. Digital receiver technologies can be equally applied to optical access networks, which share many traits with dynamic core networks. A dual-rate digital receiver, capable of detecting optical packets at 10 and 1.25 Gb/s, was developed and characterised.
Mobile ad-hoc Networks (MANETs) are well-liked networks in all types of wireless networks. To transfer data in Mobile ad-hoc Networks, widely used protocol is Dynamic Source Routing (DSR). A field which plays very important role in data transfer is “Hop limit” which is applied in each Route Request message of DSR. It controls the propagation of Route Request according to a specific value which is mentioned in Time-to-Live (TTL) portion of IP header.
This book demonstrates that American agricultural development was far more dynamic than generally portrayed. In the two centuries before World War II, a stream of biological innovations revolutionized the crop and livestock sectors, increasing both land and labor productivity.
This book is the contribution of Ms.Sangheethaa S as part of her Ph.D work. This book explains the Dynamic Source Routing based protocol for Mobile ad-hoc networks. The approach uses mobile agents for finding routes. This book also gives the simulation results done using ns2. This book will be of great help for those who wish to do research in ad-hoc networks and routing protocols of ad-hoc networks. It talks about vulnerabilities of ad-hoc networks and gives more scope for future research in this area.
World today can be described as interactions of many entities such as humans, animals, smartphones interacting among themselves. Interactions that occur regularly typically correspond to significant, yet often infrequent and hard to detect interaction patterns that are interesting to know in order to understand and predict behaviors of entities. To identify these regular behaviors, the book presents the periodic subgraph mining problem in a dynamic network and an efficient algorithm to solve it. A dynamic network is a temporal sequence of graphs that represents interactions among individuals of a population over the time. Social network analysis is probably the most famous example of dynamic network analysis. The book proposes the applications of the problem on some real-world networks and shows that analyzing interesting and insightful periodic interaction patterns uncover and characterize the natural periodicities of systems.
Elastic Optical Networks (EON) is widely accepted today as the next step in the evolution toward high capacity networking. The idea behind it is to optimize the use of resources by flexibly assigning spectrum, data rate and modulation format adapted to the needs of the end-to-end connection requests. This book is devoted to the study of three important issues in EONs, 1) dynamic source aggregation of sub-wavelength connections, 2) correlation between traffic granularity and defragmentation periodicity and 3) using spectrum fragmentation to better allocate time-varying connections.
The increasing interest in dynamic circuit networks to support the high-speed and predictable-service requirements of applications leads to the creation of various experimental test beds. One area of networking research in these testbed projects is bandwidth sharing mechanisms. One reference point for dynamic bandwidth-sharing mechanisms in connection-oriented networks is the telephone network. The bandwidth-sharing mechanism used in the telephone network is referred to as the immediate-request mode because there is no provision for making advance reservations for circuits. While this mechanism can be used in new high-speed optical connection-oriented networks, some applications require high per-call bandwidth, and this bandwidth level is a large fraction of even the highest-rate links in use today, the combination of which makes the immediate-request mode unsuitable. Therefore, alternative bandwidth-sharing modes are needed for these networks and applications. This book discusses two new bandwidth-sharing mechanisms based on book-ahead reservation schemes, and presents both the analytical and simulation models for the two mechanisms.
Sensors are low cost tiny devices with limited storage, computational capability and power. They can be deployed in large scale for performing both military and civilian tasks. The main concern in Wireless Sensor Networks is how to utilize the limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network, have drawn attentions of many research studies. The neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics show great analogy and compatibility between wireless sensor networks and neural networks. This book main focus is to present the most important possible application of neural networks in reduction of energy consumption.
Dynamic loads can cause severe damage to bridges and lead to malfunction of transportation networks. A comprehensive understanding of the nature of the dynamic loads and the structural response of bridges can prevent undesired failures while keeping the cost-safety balance. Dissimilar to the static behavior, the dynamic response of bridges depends on several structural parameters such as material properties, damping and mode shapes. Furthermore, dynamic load characteristics can significantly change the structural response. In most cases, complexity and involvement of numerous parameters require the designer to investigate the bridge response via a massive numerical study. This study targets three main dynamic loads applicable to railway and highway bridges, and explores particular issues related to each classification: seismic loads, vehicular dynamic loads, and high-speed train loads.