Social networks are broadly speaking characterized as systems that enable people to engage and communicate with one another. Recently, the media has generated a boom around various social media platforms, and it has permeated our daily lives. These types of networks, such as Facebook, Twitter, LinkedIn, and many others, are currently vying for popularity.
Best Social Network Analysis Software
For social network analysis, there are many different free programs accessible. We’ve hand-picked the best social network analysis tools for your consideration. Most of these tools are open source and free to use.
AutoMap is a visualization tool that shows the words in a text by using pictures. It makes the visualization using voice tagging. It employs proximity analysis to build the network graph of the words in a text. Proximity analysis provides the range in which the word or object is present as well as the distance between any two things. Network connections are made by AutoMap, and typically, these connections are made using proximity analysis. The output is a.csv file, and the input is a text document. Only Windows is supported by AutoMap. It has a commercial application. The main audience is students.
A 3D visualization tool for network data is called Gephi. It is a visualisation tool that is interactive. Gephi is a Java application that runs on the NetBeans platform. Based on the centrality metric, the graph is generated. The number of degrees a node has is its centrality. The use of Gephi is free. It is a quick piece of interactive visualization software. On Linux, Mac, and Windows, Gephi functions. The output formats are.gdf,.png, and.svg, while the input formats include.gml,.gdf,.csv, etc. Gephi uses Java and OpenGL as its programming languages.
A program used for network analysis is GraphMatcher. Python is the programming language that it employs. For aligning two or more graphs, use GraphMatcher. Both directed and undirected graphs are possible. GraphML serves both the input and output formats. GraphMatcher is compatible with a number of systems, including Windows, MAC, and Linux. Java must also be set up on the computer. Use of GraphMatcher is cost-free.
The graph is seen using the Graph-tool. It is a module for Python. Its programming languages are Python and C++. Tiago P. Peixoto is the one who created it. Its fundamental algorithms and data structures are all implemented in C++. The input is in GraphML format. The output is in the.bmp,.jpeg,.png formats. On Linux and Mac, Graph-tool is functional. For quick performance, its core was created in C++. The performance of multi-core designs is primarily enhanced by Open Multi-Processing, which Graph-tool enables. Both directed and undirected graphs can be created and modified with Graph-tool.
Simply said, Graphviz is a collection of open-source tools created by AT&T Labs. It is used to draw graphs that are primarily specified in scripts written in the DOT language. It is free to use Graphviz. The Eclipse Public License governs its licensing. The visualization tool for social networks is called Graphviz. It creates digraphs using straightforward text. GraphViz is the input format (.). The output formats include.bmp,.jpeg,.png, and.svg. On Linux, Mac, and Windows, Graphviz functions. Graphviz is a free software program that was created in C.
Microsoft Excel is used with the social network visualization tool NodeXL. It is one of the most well-liked packages. The Social Media Research Foundation developed it, and the Microsoft Public License governs its use. It’s written in C#, a computer language. One of the visualization tools for network analysis is NodeXL. It determines a directed graph’s corset and adds it to Excel as a chart. The input file types include.csv,.txt,.xls, etc. The output is in the.csv,.txt,.cls, etc. formats. NodeXL is free software that runs on Windows.
All that NetMiner is is a piece of application software. It is mostly utilized for social network analysis, which calls for the presentation of huge amounts of network-based data. It was made public in 2001. Java is the programming language used by NetMiner. The main purpose of NetMiner is to visualize huge data networks. Software with a free trial is what it is. Working on Windows is NetMiner. The file types for the input and output are.xls,.xlsz,.csv, etc.
Python is the programming language used by NetworkX, a Python library. It is mostly employed for researching graphs and networks. It is distributed under a BSD license and is free and open-source. The main use of NetworkX is the investigation of whole networks. It is installable and workable with Anaconda. Another tool for constructing, studying, altering, and visualizing the graph is NetworkX. Python may be used to process graphs using NetworkX. It produces a 2D or 3D version of the graph. NetworkX is a free software program. GML is used as both the input and output format.
There are numerous well-known packages for the R language that are useful for social network analysis. The igraph package in R can also be used for social network analysis. It aids in the SNA’s painless deployment. Here, large graphs with a great number of vertices can be handled with ease. R, Rdata, RDS, and RDA are the file extensions for the input and output.
Social Network Analysis Software Free
Most effective for: Examining online interactions with significant online communities
With the help of publicly accessible social media posts, Netlytic, a cloud-based social network analyzer, can automatically summarize textual data and identify communication networks.
It gathers posts from YouTube, Twitter, and RSS feeds using APIs. Existing datasets from Google Sheets or text/csv files can also be uploaded.
- Discovers popular topics or hot discussions
Maps geo-coded social media data
Allows you to conduct social science research on online communities
Overall, the platform can help you find and visualize who is talking to whom within a community, how they are talking about, how often they are communicating, how they actually feel about the topic they are discussing, and the behavior and strength of their interactions with each other.
The best method for: studying large networks with more than 10 million nodes and 100 million edges
A Python library called NetworkX is used to build and examine the dynamics and structure of complicated networks. It is a framework for social network analysis that is relatively scalable, portable, and reasonably effective.
For many purposes, NetworkX offers a standard programming interface and a graph implementation. Computer scientists, physicists, mathematicians, and social scientists are the main users.
- Includes many standard graph algorithms
- Network structure and analysis measures
- Construct random graphs and synthetic networks
This tool makes it easy to draw networks in 2D and 3D, and find subgraphs, cliques, and k-cores. You can also explore degree, center, betweenness, radius, diameter, adjacency, etc.
Best for: Constructing sophisticated analyses and models on large datasets
You can combine complex networks with many different forms of attribute data using Cytoscape to display them. You can import network and annotator data by directly connecting it to external public databases.
Despite being created initially for biological research, Cytoscape has evolved into a common platform for complicated network analysis and visualization. There are several plugins available for a variety of issue domains, including bioinformatics, social network analysis, and semantic web.
- Zoom in/out and pan to browse the network
- Filter the network to select subsets of nodes and/or interactions
- Export your networks as JSON files
This tool allows you to customize network data display the way you want. Expression data can be mapped to node color, border color, or border thickness, etc., based on user-configurable colors and visualization schemes.
Best for: Discovering relational and structural patterns in data that describe entities and relationships
A labeled, directed graph is used by Subdue, a graph-based knowledge discovery system, to describe data. This graph, like all others, has vertices and edges that stand in for various things and relationships.
Subdue can carry out various learning tasks, ranging from clustering and graph grammar learning to supervised and unsupervised learning,
- Discovers repetitive and interesting patterns within a graph
- Finds discriminating patterns from a set of classified graphs
- Identifies both exact and inexact (isomorphic) substructures within a graph
Apart from social network analysis, it has been successfully applied in a number of fields, including anomaly detection, CAD circuit analysis, protein structure analysis, and DNA gene transcription sites.
Best for: Implementing various graph layout types
Data can be visualized as simple diagrams using the open-source platform Graphviz. In many different industries, particularly engineering, these diagrams can be useful.
The DOT language, a graph description language, is a component of Graphviz. The platform offers a few tools for processing and creating DOT files. For instance, “Dott” provides a graphical user interface for viewing and editing graphs.
- Offers numerous libraries and tools, including those for utility, graphics, and drawing
- Unique ability to connect several graphs by creating nodes and edges
Saves diagrams in common formats such as images and SVG for web pages
It also has several useful options to create or edit graphs, such as tabular node layouts, hyperlinks, color, fonts, line styles, custom shapes, etc.
Ideal for: Confirmatory and exploratory study of large-scale network data
You may visually and interactively study complex network data using NetMiner. You can use it to study up to 5000 nodes and find the network’s hidden patterns and structures.
There are a few extensions for NetMiner that increase its functionality and aid in your study. For instance, NetMiner SNS Data Collector collects information from YouTube, Twitter, Facebook, and Instagram to analyze popular opinion. Biblio Data Collector makes it simple to complete difficult, time-consuming activities.
- Comprehensive network measures and models
- Pre-designed statistical procedures and charts
- What-if network analysis
A dataset containing numerous data elements is contained in the NetMiner data structure. Each of these things serves as the fundamental analytical unit. The main node set, subnode set, one-mode network, and two-mode network are all included in the data items. The dataset is used for all analysis and visualization. The project-managed basic data file is saved with the filename extension NMF (NetfMiner File).
The software has a 14-day trial period even if it is not entirely free.
Best for: Discovering the hottest, most popular Twitter conversations.
A social media analytics software called SocioViz is made for online journalists, social scientists, and media marketers.
They may quickly examine any term, subject, hashtag, or fan page with its assistance. Additionally, it automatically gathers global trends.
The platform identifies key influencers and their opinions. You can use this feature to hear what people are saying about your brand and competitors.
- Search any keyword or hashtag and filter results by data and language
- Collect posts in real-time or search one week in the past
- Identify most relevant actors and contents in online conversations
The data can be exported in three different formats: PNG, GML, and GEXF (Graph Exchange XML Format).
Best for: Increasing the understanding of network phenomena by network scientists and engineers
An open-source toolkit called NetworkKit can analyze networks with a thousand to several billion edges.
It makes use of multicore architectures by parallelizing the implementation of various effective graph algorithms. These methods calculate important network metrics like centrality measures, degree sequences, and clustering coefficients.
Some applications function quite well. For instance, community discovery in a 3.3 billion edge web graph on a 16-core server can be completed in under three minutes.
- Includes community detection algorithms
- Describes how networks form and evolve specific structural features.
- Seamlessly integrates with Python libraries for scientific computing and data analysis
That connect two separate modules, NetworkKit provides procedures to convert graph objects to NetworkX. Utilizing the Profiling Module is the most efficient way to acquire a complete view of a network. However, it can make more sense to compute them independently if you are only interested in a tiny fraction.
Most effective for: Managing graph evolution
The dynamics of graphs are the focus of the Java package GraphStream. It provides an easy way to handle and represent complex graphs. Numerous data attributes, including integers, strings, and objects, can be stored in the graph elements.
The graph is defined by GraphStream using “stream of graph events” in addition to nodes and edges. Events are changes to nodes, edges, or any other related components. In this approach, it describes not only a fixed representation but also the full history of how graph elements have changed through time.
Events Found in GraphStream
- Node/Edge addition/removal
- Clear graph
- Graph/node/edge attribute addition or alteration
- Graph/node/edge attribute removal
The library also has features to display graphs beautifully. The viewer displays nodes in an automatic layout, but you can customize the rendering of elements using a CSS stylesheet.
Best for: Representing, modeling, and mapping complex networks
R programming language is packed with numerous packages relevant for social network analysis:
- igraph for generic network analysis
- network for manipulating and displaying network objects
- sna for performing sociometric analysis
- tnet for performing analysis of weighted or longitudinal network
- Bergm for Bayesian analysis for exponential random graph models
- networksis for simulating bipartite networks with fixed marginals
Each of the currently available software (that are applicable for social network analysis) has its own features. They may be used to calculate many different specialized properties, including centrality, clustering coefficient, network diameter, density, page level, and many more.
The R programming language makes it very simple to modify, restructure, and apply customized functions since network data are less restricted than typical social science data. The platform is open source and platform-neutral, making it accessible worldwide.
Best for: Drawing a 3D layout of graphs
Pajek is an excellent program for analyzing and visualizing large networks. It has three main goals:
- Split a large network into smaller ones that can be processed efficiently
- Provide users with powerful visualization tools
- Implement a set of efficient (sub-quadratic) algorithms to examine large networks
Pajek allows you to identify clusters in networks, extract and display vertices from the same clusters independently (in-depth local view), reduce vertices inside clusters, and display relationships between clusters (global view).
Some of the fundamental operations include looking for connected components, looking for shortest paths, maximum flow, looking for k-neighbors, centralizing networks, quickly multiplying sparse networks, and creating various kinds of random networks.
Pajek supports two-mode networks (bipartite graphs) in addition to common (directed, undirected, and mixed) networks and temporal networks (dynamic graphs).
Pajek has been employed thus far in a variety of research fields, including social network analysis, biomedical and genetic research (protein-receptor interaction networks), genealogy, and data mining (2-mode networks).
Creating social data connectors to map communication between organizations and small-world networks is the best use of social data.
Users interact with representations, change the structures, shapes, and colors to uncover hidden patterns in Gephi, which is similar to Photoshop except for graph data. A 3D render engine is used by this open-source program to display graphs in real-time and hasten exploration.
It is specifically made to help data scientists and analysts form hypotheses, identify patterns, and pinpoint structural singularities or flaws in data collection.
- Real time visualization for networks up to 100,000 nodes and 1,000,000 edges
- Includes state-of-the-art algorithms layout algorithms
- Allows you to create complex filter queries without scripting
In addition to conventional network analysis, Gephi has been used in various research projects in journalism and academia. For example, it has been used to represent patterns of biological data, examine Twitter network traffic during social unrest, and visualize the global connectivity of New York Times content.
Social Network Analysis and Visualization Tools
While there are various libraries for social network analysis, R is a general-purpose analytics tool. The names of these are degreenet, RSeina, PAFit, igraph, sna network, tnet, ergm, Bergm, hergm, latentnet, and networksis. Each offers specialized functionality and is a valuable collection of information for those who are familiar with R.
SocNetV (Social Networks Visualizer)
A cross-platform, user-friendly program for social network analysis and visualization is called SocNetV (Social Networks Visualizer). It enables you to load networks in a variety of formats or create networks (mathematical graphs) on a virtual canvas (GraphML, GraphViz, Adjacency, Pajek, UCINET, etc). You can also alter social networks, examine their sociological and mathematical aspects, and use visualization layouts using SocNetV.
A social media analytics tool called Socioviz uses indicators from social network analysis. enables users to search Twitter conversations for important influencers, viewpoints, and content. User mention and hashtag copresence social network graphs are displayed and exportable in Gephi format (gexf) for additional research.
Advanced Link Analysis, Data Visualization, Geospatial Mapping, and SNA are all performed with Sentinel Visualizer. With its database-driven data visualization platform, you can easily model various connection types and view multi-level links between items. The most crucial items are highlighted in optimum views created using advanced drawing and redrawing techniques.
For network analysis and statistical network modeling, Statnet is a collection of R software tools. The analytical approach is based on models of random graphs from the exponential family (ergm). With tools for model estimation, model evaluation, model-based network simulation, and network visualization, it offers a thorough framework for ergm-based network modeling. A central Markov chain Monte Carlo (MCMC) method drives this extensive capability.
SVAT (Smart Visual Analytics Tool)
Data visualization, fraud investigation, and other uses are all possible using SVAT (Smart Visual Analytics Tool). It offers affordable, user-friendly display of subject-to-subject flows and linkages. In many instances, it is essential to have a chronological perspective of the depicted dataset. There are two main timeline views supported by SVAT, each with a wide range of choices. It can crunch data from both organized and unstructured sources to find hidden patterns.
Tulip is an information visualisation framework dedicated to the analysis and visualisation of relational data. It aims to provide the developer with a complete library, supporting the design of interactive information visualisation. Written in C++ the framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualisations. One of the goal of Tulip is to facilitate the reuse of components and allows the developers to focus on programming their application. This development pipeline makes the framework efficient for research prototyping as well as the development of end-user applications.
Visone is a software for the visual creation, transformation, exploration, analysis, and representation of network data, jointly developed at the University of Konstanz and the Karlsruhe Institute of Technology since 2001.the main purpose of the Visone software is to empower researchers in the social sciences to analyze and visualize network data in an integrated fashion. Potential applications range from sociometry to bibliometrics and web analysis.
XANALYS specialise in providing powerful software capabilities. From threat assessment, Investigative major case management and advance crime and fraud analytics. It helps to manage multi-jurisdiction major crime investigations, evaluate and analyse suspicious financial transactions, capture and act upon intelligence reports, and disclose evidence in a court-ready format to ensure successful outcomes.
Social Network Visualization
The social network visualization is an excellent way to show the relationships between your friends. You can see who you talk to the most, and who you don’t talk to at all. You can also see if there are any people in your network who might be able to introduce you to new people.
The social network visualization is a great tool for showing how people are connected to each other, and the strength of those connections.
In this example, we can see that people who are more connected are clustered in the center. The more connections they have with others, the more central they are. In this example, there’s a clear divide between two groups: people with more connections (blue dots) and people with fewer connections (orange dots).
This visualization is useful because it helps us understand how people are connected to each other within a system. It also allows us to see how strong those connections are by looking at how many lines connect one person to another.
It’s no secret that social networks have become a huge part of our lives. We use them to stay in touch with friends and family, get news, find new jobs and opportunities, and even just to pass the time. It’s no wonder they’re so popular—they help us connect with others around the world, no matter where we are in life or what we’re doing at any given moment.
But how exactly do all those connections work? How does Facebook know your best friend from high school is getting married? And why does LinkedIn keep sending you emails about job openings that don’t seem quite right for you? What kind of information do these sites have about you, anyway?
We’ve put together this visualization tool to help you see the relationships between different users on various social networks and platforms. The network displays “nodes” representing users and “edges” representing connections between them, allowing you to better visualize who knows who and how often they interact with each other. It can also be helpful if you’re trying to find out more about a particular person or group. For example, if you wanted to know more about your favorite celebrities’ fans, it would be useful to use social network visualizations in order to get an idea of who their most popular fans are and what kind of things they might like doing together.
What is SocNetV?
A cross-platform tool for social network analysis and visualization, Social Network Visualizer (SocNetV) is an open-source project that primarily targets social researchers. It was created using the open-source C++ and Qt programming toolkit and works on Windows, Linux, and OS X.
With SocNetV, you may create social networks with a few clicks on a virtual canvas or load networks in a variety of formats (such as GraphML, GraphViz, Adjacency, EdgeList, Pajek, UCINET, GML, etc.; see more under Supported Formats) and customize them to your specifications.
Different random network generation models (Barabási-Albert Scale-Free, Erds-Rényi, Watts-Strogatz Small-World, d-regular, ring lattice, etc.) can be used to generate random networks. Find out more about them in the section on creating random networks.
The program calculates common metrics for network cohesion and graph theory, including density, diameter, geodesic distances, clustering coefficients, walks, connectedness, eccentricity, etc. Cohesion measures has further information.
Additionally, it provides structural data, including measures for node and network centrality and prestige, such as betweenness, eigenvector and closeness centrality, proximity, and pagerank prestige. Centralities and Prestige has further information.
Included are quick community recognition algorithms like triad and clique censuses. community detection for additional information.
With SocNetV, you can also carry out structural equivalency analysis utilizing Pearson coefficients, actor similarity and tie profile dissimilarity, hierarchical clustering, and other techniques. Structural Equivalence approaches have additional information.
SocNetV offers a number of layout methods and models for the meaningful presentation of social networks. For instance, you may include a layout model based on a prominence index (radial, level, nodal sizes, and color) (i.e. Degree Centrality score). Alternatively, you might select one of the force-directed placement algorithms (i.e. Kamada-Kawai Spring Embedder, Fruchterman-Reingold, etc). Visualization and layout algorithms have more information.
When you can’t see your customers, it’s difficult to concentrate on your business. Software for social network analysis can help with it. No matter where in the world your target market is located, attractive social media analysis tools will assist you in tracking the major trends and insights linked to them. You can use this software to monitor what people are saying and how they engage with one another on Facebook, Twitter, LinkedIn, and any other social media platform.