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Cytoscape. Originally designed to be used for biological research, Cytoscape is now used for complex network analysis and visualization. Use Cytoscape for data integration, analysis, and visualization. Increase the functionality by installing apps, which are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases.
GraphViz. This visualization tool represents structural information as diagrams of abstract graphs and networks. There are many options for graph layouts.
Kumu. Kumu allows you to map the relationships between people, systems, and concepts. You are able to import your data and/or build networks using live data, use built-in metrics to identify key elements and connections, and customize your visualizations with dropdowns, buttons, text, images, and more. View their pricing plans or have unlimited public projects for free.
Nodegoat. Nodegoat is a web-based tool that enables you to create and manage datasets. You can instantly analyze and visualize datasets and create relational, geographical, and temporal attributes.
NodeXL. An add-in for Windows versions of Excel to do network graphs. It is a project from the Social Media Research Foundation and is optimal for social media analysis. Allows general network visualization, grouping, and basic analyses (e.g., density, degree). A pro version is licensed per year and includes additional functionalities.
Robert A. Hanneman and Mark Riddle, Introduction to Social Network Methods(2005). A good introductory but thorough text that uses UCINet to go over practice problems and examples. Also details more advanced topics such as clique identification, structural equivalence or block-modelling.
A User's Guide to Network Analysis in R by Douglas Luke
Publication Date: 2015-12-14
Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.
Coursera, Social and Economic Networks. A more advanced and theoretical course that delves deeper into the mathematical foundations of network structure and analysis. Assumes some level of familiarity with linear algebra, probability and statistics concepts.
Future Learn, Social Media Analytics. This course is focused on social media analysis, and specifically uses Twitter data, but it also reviews some network analysis concepts and shows how to perform analyses of social data in Tableau and Gephi.
Miriam Posner, Cytoscape Tutorials (2016). These tutorials provide a basic introduction to using Cytoscape to conduct network analysis of humanistic data.