Social network analysis software typically uses network and graph theory to investigate social structures both analytically and visually. The main constructions are the nodes (the entities we are interested in, usually people) and the ties or edges that connect them. Many of the products listed here are open source with a license that allows free use in commercial settings.
Squidis a network workbench application that visualizes networks with some of the most popular design algorithms. It enables detailed visualizations of network data, interactive layout manipulation, graph editing, and process visualization, as well as different text input and output methods using Tikz and PSTricks. It is developed by the ETH Zürich Chair of Systems Design, a research group that applies a complex systems approach to investigate economic and social networks.
citoscapeis an open source software platform for visualizing complex networks and integrating them with any type of attribute data. There are many applications available for various types of problem domains, including bioinformatics, social network analysis, and the semantic web.
Where?is an interactive platform for visualization and exploration of all types of networks and complex systems, dynamic and hierarchical graphics. It runs on Windows, Linux, and Mac OS X. Gephi is free and open source. It supports all types of networks: directed, undirected, and mixed, and is capable of handling very large network graphs of up to a million nodes. Various metrics are supported, including matchmaking, closeness, diameter, clustering ratio, average shortest path, page rank, and HITS. Dynamic filtering allows you to select edges and/or nodes based on the structure or data of the network. Ideal for social network analysis, link analysis, and biological network analysis. Perhaps the most advanced of the open source tools.
graphic toolis an efficient Python module for statistical analysis and manipulation of graphs (also known as networks). Unlike most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This gives it a comparable level of performance (both in memory usage and computation time) to that of a pure C/C++ library. Graph-tool has its own versatile and interactive drawing routines and layout algorithms based on cairo and GTK+, but it can also function as a very handy interface to the excellent graphviz package.
ChartChiYou can run very large graphics calculations on a single machine, by using a novel algorithm to process the graphics from disk (SSD or HDD). Programs for GraphChi are written in the vertex-centric model, proposed by GraphLab and Google's Pregel. GraphChi runs vertex-centric programs asynchronously (ie changes written to the edges are immediately visible for subsequent computation) and in parallel. GraphChi also supports streaming graph updates and removing graph borders. GraphChi's promise is to bring web-scale graphing computation, like social media analytics, within the reach of anyone with a modern laptop.
graphicvizis open source graph visualization software that represents structural information such as network diagrams and abstract graphs. Graphviz design programs take graph descriptions in a simple text language and render diagrams in useful formats, such as images and SVG for web pages; PDF or Postscript to include in other documents; or display in an interactive graphical browser. Graphviz has many useful features for specific diagrams, such as color options, fonts, tabular node layouts, line styles, hyperlinks, and custom shapes.
JUNG– Java Universal Network/Graph Framework: is a software library that provides a common and extensible language for modeling, analyzing, and visualizing data that can be represented as a graph or a network. It is written in Java, which allows JUNG-based applications to use the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries.
The JUNG architecture is designed to support a variety of representations of entities and their relationships, such as directed and undirected graphs, multimodal graphs, parallel-edge graphs, and hypergraphs. Provides a mechanism to annotate graphics, entities, and relationships with metadata. This makes it easy to create analytical tools for complex data sets that can examine the relationships between entities, as well as the metadata attached to each entity and relationship.
libSNAis an open source library for social media analytics, licensed under the LGPL. This library is being actively developed by Abe Usher in the hope that it will serve as a catalyst for improving the field of social network analysis.
- Easy to use Python API
- Flexible data import options
- integrated reports
- Integrated data export capabilities
- Open source: easily extensible
- Fast processing time (efficient use of graphics algorithms)
MiniumIt is suitable for many types of network analysis, including social networks. It does provide filtering mechanisms, interactive editing, support for dynamic networks, various metrics, and automatically detects communities.
- network visualization with multiple layouts
- interactive network edition
- support for dynamic grids (multiple time frames)
- network filtering
- calculates different measures of centrality (network metrics and statistics)
- automatically detect communities (community mining)
- shows community dynamics over time (analysis and visualization of community events)
netliticis a cloud-based social media visualizer and text parser. Netlytic can automatically summarize large volumes of text and discover and visualize social networks from conversations on social networking sites such as Twitter, YouTube, blog comments, online forums, and chat rooms. It is designed to help researchers and others understand how an online group works, identify key and influential components, and discover how information and other resources flow in a network.
Kit de redis a growing open source toolkit for high performance network analysis. NetworkKit is a Python module. It implements efficient graphics algorithms, many of them parallel to use multicore architectures. These are intended to compute standard measures of network analysis, such as degree sequences, clustering coefficients, and centrality. The high-performance algorithms are written in C++ and exposed to Python through the Cython toolchain.
RedXis a software package in the Python language for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Features include:
- Python language data structures for graphs, digraphs, and multigraphs.
- Many standard graphics algorithms
- Network structure and analysis measures.
- Classic graph generators, random graphs and synthetic networks
- Nodes can be "anything" (eg text, images, XML records)
- Borders can contain arbitrary data (eg weights, time series)
NodoXLis a free, open source template for Microsoft® Excel® 2007, 2010 and 2013 that makes it easy to explore network graphs. With NodeXL, you can enter a list of network edges into a worksheet, click a button, and view your graph, all in the familiar environment of the Excel window.
Flexible import and export Import and export graphs in GraphML, Pajek, UCINet and matrix formats.
Direct connections to social networks Import social networks directly from Twitter, YouTube, Flickr and email, or use one of several available plugins to obtain networks from Facebook, Exchange, Wikis and WWW hyperlinks.
Zoom and Scale Magnify areas of interest and scale graph vertices to reduce clutter.
Flexible Layout Use one of several "force-directed" algorithms to layout the graph, or drag the vertices with the mouse. Have NodeXL move all the smaller connected components of the graph to the bottom of the graph to focus on what's important.
Easily adjustable appearance Set the color, shape, size, label, and opacity of individual vertices by populating worksheet cells, or let NodeXL do it for you based on vertex attributes such as the degree, betweenness centrality or PageRank.
Dynamic Filtering Instantly hide vertices and edges using a set of sliders; for example, hide all vertices with a degree less than five.
Powerful Vertex Clustering Cluster graph vertices by common attributes, or have NodeXL analyze their connectivity and automatically group them into groups. Make groups stand out using shapes and colors, control them with a few clicks, or place each group in its own box within the chart. "Bundle" intergroup borders to make them more manageable.
Graph Metrics Calculations Easily calculate degree, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, clustering coefficient, graph density and more.
Task Automation Perform a set of repeating tasks with a single click.
A spider– suite of programs for analysis and visualization of very large networks.
RIt is a general purpose analytics software, but there are several libraries available for social media analytics. These include gradenet, RSeina, PAFit, igraph, sna network, tnet, ergm, Bergm, hergm, latentnet, and redingis. Each provides specialized functionality, and to those familiar with R they represent a rich set of resources.
Social Media Viewer(SocNetV) is an easy-to-use multiplatform tool for the analysis and visualization of Social Networks. It allows you to build networks (mathematical graphs) with a few clicks on a virtual canvas, or load networks of various formats (GraphML, GraphViz, Adjacency, Pajek, UCINET, etc.). In addition, SocNetV allows you to modify social networks, analyze their social and mathematical properties, and apply display layouts for relevant presentations.
Furthermore, random networks (Erdos-Renyi, Watts-Strogatz, ring network, etc.) and known social network data sets (ie Padgett's Florentine families) can be easily recreated. SocNetV also offers a built-in web crawler, allowing you to automatically create networks from links found at a given starting URL.
The application calculates basic graphical properties, such as density, diameter, geodesics and distances (geodesic lengths), connection, eccentricity, etc. It also computes advanced structural measures for social network analysis, such as centrality and prestige indices (i.e., closeness centrality, , information centrality, power centrality, proximity, and prestige rank), triad census, cliques, coefficient grouping, etc.
SocNetV offers various layout algorithms based on prominence indexes or dynamic models (i.e. Spring-embedder) for meaningful social media views. There is also comprehensive documentation, both online and while the application is running, explaining every feature and algorithm of SocNetV in detail.
sociovizis a web-based Twitter analytics platform powered by social media analytics metrics. It allows the user to check Twitter conversations and find the most influential people based on who replies to whom and who mentioned whom. Social media graphs (user mention and hashtag co-presence) are visualized and can be exported in Gephi (gexf) format for further analysis.
the stateis a set of network analysis software packages that implement recent advances in statistical network modelling. The analytical framework is based on random graph models of the exponential family (ergm). statnet provides a comprehensive framework for ergm-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This extensive functionality is driven by a core Markov Chain Monte Carlo (MCMC) algorithm.
MASTERis a graph-based knowledge discovery system that finds structural and relational patterns in data that represent entities and relationships. SUBDUE represents data using a labeled directed graph in which features are represented by labeled vertices or subgraphs, and relationships are represented by labeled edges between features. SUBDUE uses the principle of minimum description length (MDL) to identify patterns that minimize the number of bits required to describe the input graph after being compressed by the pattern. SUBDUE can perform various learning tasks, including unsupervised learning, supervised learning, clustering, and graphical grammar learning. SUBDUE has been successfully applied in several areas, including bioinformatics, web structure mining, counter-terrorism, social network analysis, aviation, and geology.
Tulipis an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a comprehensive library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems you are addressing. Written in C++, the framework enables the development of algorithms, visual codings, interaction techniques, data models, and domain-specific visualizations. One of Tulip's goals is to facilitate component reuse and allow developers to focus on their application programming. This development pipeline makes the framework efficient for research prototyping as well as end-user application development.
minkis a software tool for research and teaching in social network analysis. It is specifically designed to enable experts and novices alike to apply innovative and advanced visual methods with ease and precision. Main features include:
- interactive graphical user interface, adapted to social networks
- innovative network visualizations
- unconfirmed relationship support
- available in Java for Windows, Linux and MacOS
- import and export of standard formats for social media data
- publication-quality export in JPEG, PDF, SVG, metafile, and other formats
SquidcitoscapeWhere?graphic toolChartChigraphicvizJUNGlibSNAMiniumnetliticKit de redRedXNodoXLA spiderRSocial Media Analytics SoftwaresociovizSocNetVthe stateMASTERTulipmink