Abstract
Composed by one of the most prominent network scholars, this book starts with an inspiring personal introduction where Albert-László Barabási relates how network science emerged and maps his own scientific contributions to it. Network Science is a textbook intended for graduate and advanced undergraduate students coming from different disciplines. The book is written in accessible language, terminology is clearly explained, and concepts are illustrated with examples from everyday life as well as multiple figures. Each chapter includes the main body that introduces, discusses, and summarizes its theme, homework assignments, and advanced topics that explain more complex concepts and often involve more elaborate quantitative components. Better yet, the author’s website provides much additional material, including lecture slides he uses to teach network science, software tutorials for network analysis and visualization, and references to other useful resources, not to mention all the chapters of the book (http://barabasi.com/book/network-science).
After presenting network science as an interdisciplinary, quantitative approach to understanding complex systems (Chapter 1), the book introduces its basic terminology coming from graph theory, such as node, edge, node degree, adjacency matrix, edge weight, bipartite network, path, connectedness, and clustering (Chapter 2). Chapter Three describes the random network model and the evolution and main properties of random networks and explains that while real networks are not random, the model has been serving as a mathematical tool for analyzing complex networks. As shown in Chapter Four, many real-life networks are scale-free: they include very few nodes with extremely large numbers of links while the vast majority of nodes have just a few connections. The Barabási-Albert model explains the mechanisms of scale-free network formation and suggests that understanding network evolution is the key to understanding its resultant topology (Chapter 5).
Chapter Six presents models that account for network growth, node deletion and aging, links formation between existing nodes, and initial node attractiveness as factors in network structure. Chapter Seven discusses degree correlation in networks when highly connected nodes tend to connect to each other (degree assortativity) or when nodes with high degree are likely to have links to low-degree ones (disassortative mixing). The rest of the book discusses network robustness and cascading failures (Chapter 8), communities within networks and clustering algorithms (Chapter 9), and models of diffusion with the focus on epidemics and the role of network topology in contagion (Chapter 10).