{"product_id":"network-science-a-mathematical-and-computational-introduction-to-graphs-and-complex-systems-using-r-9798259181533","title":"Network Science: A Mathematical and Computational Introduction to Graphs and Complex Systems using R","description":"\u003cp\u003e • Author(s): William Franz Lamberti\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - Algorithms\u003c\/p\u003e\u003cp\u003eMaster Network Science Through Mathematics, Computation, and R\u003c\/p\u003e\u003cp\u003e\u003ci\u003eNetwork Science: A Mathematical and Computational Introduction to Graphs and Complex Systems using R\u003c\/i\u003e is a rigorous yet accessible textbook that teaches the mathematical foundations of graph theory and network analysis alongside hands-on R programming. From the structure of social networks to the mathematics of clustering and connectivity, every concept is developed from first principles and immediately reinforced with working R code - no external packages required.\u003c\/p\u003e\u003cp\u003eInspired by the Schaum's Outlines tradition of learning through worked examples, this book pairs formal definitions and proofs with step-by-step computations so readers build both theoretical understanding and practical skill simultaneously.\u003c\/p\u003eWhat You Will Learn\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eMathematical Foundations: \u003c\/b\u003e Set theory, matrix algebra, and the graph-theoretic vocabulary underlying all of network science\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eNetwork Representations: \u003c\/b\u003e Edge lists, adjacency matrices, directed and undirected graphs, simple graphs, and multigraphs\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eNode Degrees: \u003c\/b\u003e Degree sequences, the Handshaking Lemma, in- and out-degree for directed networks\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eDegree Distributions: \u003c\/b\u003e Frequency tables, degree histograms, heavy-tailed distributions, and hub-heavy networks\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePaths and Walks: \u003c\/b\u003e Walks, trails, paths, cycles, and matrix-power methods for counting indirect connections\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTriangles and V-Shapes: \u003c\/b\u003e Local triangle counts, global triangle counts via tr(A3)\/6, and open triads\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eNetwork Clustering: \u003c\/b\u003e Local clustering coefficient, global clustering coefficient, transitivity, and how clustering differs across network types\u003c\/li\u003e\n\u003c\/ul\u003eWho This Book Is For\u003cp\u003eThis textbook is written for undergraduate and graduate students in mathematics, statistics, computer science, and data science who want a mathematically grounded introduction to network analysis. It is equally well-suited for working professionals and researchers seeking a self-contained, example-driven reference. A background in introductory linear algebra and basic probability is helpful but not required - the book builds all necessary tools from scratch.\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eWorked examples throughout: \u003c\/b\u003e Every definition and theorem is followed by fully solved numerical examples, in the tradition of Schaum's Outlines\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eBase R only: \u003c\/b\u003e All computational examples use only base R - no igraph, no tidyverse, no external packages - so readers understand exactly what the code is doing\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTheory meets computation: \u003c\/b\u003e Each mathematical result is paired with an R implementation that verifies the theory on concrete networks\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eClear visual diagrams: \u003c\/b\u003e Network graphs, Venn diagrams, and degree histograms illustrate every major concept\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eSelf-contained: \u003c\/b\u003e Mathematical prerequisites are introduced within the text, making the book accessible without a separate reference\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you are taking your first course in network science, teaching graph theory, or building a foundation for research in complex systems and data science, this book gives you the mathematical rigor and computational fluency to analyze real-world networks with confidence.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47882760683671,"sku":"9798259181533","price":4311.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798259181533.webp?v=1781096866","url":"https:\/\/atlanticbooks.com\/products\/network-science-a-mathematical-and-computational-introduction-to-graphs-and-complex-systems-using-r-9798259181533","provider":"Atlantic Books","version":"1.0","type":"link"}