Top 9 best graph analysis
Finding the best graph analysis suitable for your needs isnt easy. With hundreds of choices can distract you. Knowing whats bad and whats good can be something of a minefield. In this article, weve done the hard work for you.
Best graph analysis
1. Case in Point: Graph Analysis for Consulting and Case Interviews
Description
The use of complex graphs in case interviews has exploded. You have a very short time to look at the graph, analyze it, extract what s important and apply it to your answer. This book was designed to help you understand the role of graphs in consulting (both during an interview and on the job). The authors introduce the Ivy Graph Framework, which will allow you to analyze 11 of the most popular graphs quickly, completely, and with great confidence. In addition the book provides ten sophisticated cases with numerous graphs per case and allows you to see how these cases unfold. There is nothing else out there like it!2. Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data
Feature
WileyDescription
Wring more out of the data with a scientific approach toanalysisGraph Analysis and Visualization brings graph theory outof the lab and into the real world. Using sophisticated methods andtools that span analysis functions, this guide shows you how toexploit graph and network analytic techniques to enable thediscovery of new business insights and opportunities. Published infull color, the book describes the process of creating powerfulvisualizations using a rich and engaging set of examples fromsports, finance, marketing, security, social media, and more. Youwill find practical guidance toward pattern identification andusing various data sources, including Big Data, plus clearinstruction on the use of software and programming. The companionwebsite offers data sets, full code examples in Python, and linksto all the tools covered in the book.
Science has already reaped the benefit of network and graphtheory, which has powered breakthroughs in physics, economics,genetics, and more. This book brings those proven techniques intothe world of business, finance, strategy, and design, helpingextract more information from data and better communicate theresults to decision-makers.
- Study graphical examples of networks using clear and insightfulvisualizations
- Analyze specifically-curated, easy-to-use data sets fromvarious industries
- Learn the software tools and programming languages that extractinsights from data
- Code examples using the popular Python programminglanguage
There is a tremendous body of scientific work on network andgraph theory, but very little of it directly applies to analystfunctions outside of the core sciences until now. Writtenfor those seeking empirically based, systematic analysis methodsand powerful tools that apply outside the lab, Graph Analysisand Visualization is a thorough, authoritative resource.
3. Spatio-Temporal Graph Data Analytics
Description
This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developingscalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporalgraph data in two application domains, viz., urban transportation and social networks.Then the authors present representational models and data structures, which can effectivelycapture these semantics, while ensuring support for computationally scalable algorithms.
In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithmsinternally use the semantically rich data structures developed in the earlierpart of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discusssome open research problems in the area.
This book will be useful as a secondary text for advanced-level students entering into relevantfields of computer science, such as transportation and urban planning. It may alsobe useful for researchers and practitioners in the field of navigational algorithms.
4. Probabilistic Foundations of Statistical Network Analysis (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Description
Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks.
The authors incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics.
Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Cranes research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Cranes methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RANDs Project AIR FORCE.
5. Case in Point: Graph Analysis for Consulting and Case Interviews
Description
The use of complex graphs in case interviews has exploded. You have a very short time to look at the graph, analyze it, extract what s important and apply it to your answer. This book was designed to help you understand the role of graphs in consulting (both during an interview and on the job). The authors introduce the Ivy Graph Framework, which will allow you to analyze 11 of the most popular graphs quickly, completely, and with great confidence. In addition the book provides eight sophisticated cases with numerous graphs per case and allows you to see how these cases unfold. There is nothing else out there like it!6. Python for Graph and Network Analysis (Advanced Information and Knowledge Processing)
Description
This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities.
Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.
7. Graphs on Surfaces and Their Applications (Encyclopaedia of Mathematical Sciences)
Description
Graphs drawn on two-dimensional surfaces have always attracted researchers by their beauty and by the variety of difficult questions to which they give rise. The theory of such embedded graphs, which long seemed rather isolated, has witnessed the appearance of entirely unexpected new applications in recent decades, ranging from Galois theory to quantum gravity models, and has become a kind of a focus of a vast field of research. The book provides an accessible introduction to this new domain, including such topics as coverings of Riemann surfaces, the Galois group action on embedded graphs (Grothendieck's theory of "dessins d'enfants"), the matrix integral method, moduli spaces of curves, the topology of meromorphic functions, and combinatorial aspects of Vassiliev's knot invariants and, in an appendix by Don Zagier, the use of finite group representation theory. The presentation is concrete throughout, with numerous figures, examples (including computer calculations) and exercises, and should appeal to both graduate students and researchers.
8. Guide to Graph Algorithms: Sequential, Parallel and Distributed (Texts in Computer Science)
Description
This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, and approximation algorithms and heuristics for such problems. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms including algorithms for big data and an investigation into the conversion principles between the three algorithmic methods.
Topics and features: presents a comprehensive analysis of sequential graph algorithms; offers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithms; describes methods for the conversion between sequential, parallel and distributed graph algorithms; surveys methods for the analysis of large graphs and complex network applications; includes full implementation details for the problems presented throughout the text; provides additional supporting material at an accompanying website.This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms.
9. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences)
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