The availability of large data sets has allowed researchers to uncover complex properties such as large-scale fluctuations and heterogeneities in many networks, leading to the breakdown of standard theoretical frameworks and models. Until recently these systems were considered as haphazard sets of points and connections. Recent advances have generated a vigorous research effort in understanding the effect of complex connectivity patterns on dynamical phenomena. This book presents a comprehensive account of these effects. A vast number of systems, from the brain to ecosystems, power grids and the Internet, can be represented as large complex networks. This book will interest graduate students and researchers in many disciplines, from physics and statistical mechanics to mathematical biology and information science. Its modular approach allows readers to readily access the sections of most interest to them, and complicated maths is avoided so the text can be easily followed by non-experts in the subject.

## Machine Learning in Complex Networks

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

## Complex Networks

The field of complex network exploded since the 1990s, the number of publications in a variety of different areas has grown exponentially and practically, and every discipline started to recognize the presence of these mathematical structures in its area of research. Actually almost any system from the nowadays traditional example of the Internet to complex patterns of metabolic reactions can be analyzed through the graph theory. In its simplest and non rigorous definition a graph is a mathematical object consisting of a set of elements (vertices) and a series of links between these vertices (edges). This is of course a very general description, and as any mathematical abstraction, the idea is to discard many of the particular properties of the phenomenon studied. Nevertheless, this modeling is remarkably accurate for a variety of situations. Vertices can be persons related by friendship or acquaintances relations. Vertices can be proteins connected with one another if they interact in the cell. Networks have always existed in Nature of course, but it is fair to say that given the present technological explosion, they became more and more important. Starting from the Internet the web of connections between computers we started to link and share our documents through web applications and we start to get connected with a number of persons larger than usual. It is this revolution in our daily habit that made natural thinking of networks in science and research. Once this has been realized it became natural to see the cell as a network of molecular events from chemical reactions to gene expressions. The point is to establish if this new perspective can help researchers in finding new results and by understanding the development of these phenomena and possibly control their evolution. We believe that this is the case and in the following we shall provide the evidence of that. Together with applications there are of course true scientific questions attached to network theory. Consider the various ways in which the edges are distributed among the vertices: even by keeping the number of edges and vertices constant we have many different patterns possible. Interestingly some features used to describe these shapes are not related to the particular example considered, but instead they are universal. That is to say they can be found in almost any network around. In this book, we introduce the subject of complex networks and we present the structure of the associated topics that range from social science to biology and finance. We start by considering the mathematical foundations of networks and we then move to an overview of the various applications

## Computational Science – ICCS 2008

*8th International Conference, Kraków, Poland, June 23-25, 2008, Proceedings*

## Statistical Mechanics of Complex Networks

Networks can provide a useful model and graphic image useful for the description of a wide variety of web-like structures in the physical and man-made realms, e.g. protein networks, food webs and the Internet. The contributions gathered in the present volume provide both an introduction to, and an overview of, the multifaceted phenomenology of complex networks. Statistical Mechanics of Complex Networks also provides a state-of-the-art picture of current theoretical methods and approaches.

## Random Walks and Diffusions on Graphs and Databases

*An Introduction*

## Dynamical Systems on Networks

*A Tutorial*

## Evolutionary Equations with Applications in Natural Sciences

With the unifying theme of abstract evolutionary equations, both linear and nonlinear, in a complex environment, the book presents a multidisciplinary blend of topics, spanning the fields of theoretical and applied functional analysis, partial differential equations, probability theory and numerical analysis applied to various models coming from theoretical physics, biology, engineering and complexity theory. Truly unique features of the book are: the first simultaneous presentation of two complementary approaches to fragmentation and coagulation problems, by weak compactness methods and by using semigroup techniques, comprehensive exposition of probabilistic methods of analysis of long term dynamics of dynamical systems, semigroup analysis of biological problems and cutting edge pattern formation theory. The book will appeal to postgraduate students and researchers specializing in applications of mathematics to problems arising in natural sciences and engineering.

## Handbook of Optimization in Complex Networks

*Theory and Applications*