Online social graph tools

You can store various types of data attributes numbers, strings, or any object on the graph elements. Events represent when nodes, edges, or any associated components change. 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.

R programming language is packed with numerous packages relevant for social network analysis:. All existing packages that can be used for social network analysis have their own features. They can be used to compute centrality, clustering coefficient, network diameter, density, page level, and many more specific features. Since network data are less regulated than usual social science data, R programming language makes it fairly easy to manipulate and reorganize data and implement customized functions.

The platform is free and platform-independent, so it can be used anywhere. Pajek is an excellent program for analyzing and visualizing large networks. It has three main goals:. With Pajek, you can find clusters in a network, extract vertices that belong to the same clusters and show them separately in-depth local view , shrink vertices in clusters, and show relations among clusters global view.

Some of the basic operations include searching for connected components, searching for shortest paths, maximum flow, k-neighbors, centralization of networks, fast sparse network multiplication, and generating different types of random networks. Besides common directed, undirected, mixed networks, Pajek supports two-mode networks bipartite graphs and temporal networks dynamic graphs.

Best for : Creating social data connectors to map communicate organizations and small-world networks. Gephi is like Photoshop but for graph data — users interact with representations, manipulate the structures, shapes, and colors to discover hidden patterns. This open-source tool utilizes a 3D render engine to display graphs in real-time and speed up the exploration.

It is specially designed for data analysts and scientists to make hypotheses, find patterns, and isolate structure singularities or errors during data sourcing. 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.

Read: 13 Best Penetration Testing Tools. This is a Python module for the statistical analysis of complex networks. The modules use various algorithms and output formats to display the graph. It has its own layout algorithm and interactive drawing routines based on open-source graphic libraries named Cario and Graphviz. InfiniteGraph is cross-platform, cloud-enabled, designed to handle very high throughput. It can ingest massive volumes of distributed data while simultaneously building the graph to allow for real-time queries.

You can use this tool to develop, test, and deploy on up to 50 GB of disk storage. There are no time limitations or feature restrictions. Egocentric Network Study Software EgoNet allows you to create the questionnaire and gather and analyze all social network data of a website on the Internet. It also provides sets of data and comprehensive measures that can be used for further analysis by other tools.

Plus Point : Includes relationship-based survey tool for organizational network analysis. With Polinode you can upload arbitrary network data for online visualization and analysis. It can handle tens of thousands of nodes and calculate computationally demanding metrics on these networks such as centrality, closeness, betweenness, and many more.

Polinode also comes with a fully integrated relationship-based survey tool, which allows you to run surveys with tens of thousands of respondents. Mathematica provides state-of-the-art functionality for creating and analyzing complex networks.

It has about 5, built-in functions covering all areas of technical computing — all effectively integrated so they work perfectly together. High-level functions allow you to identify communities, discover cohesive groups, and visualize the results.

A full suite of social network measures makes it easy to explore networks, rank actors from their centralities, and provide recommendations based on similar actors.

Social network analysis is a research technique developed primarily in communication science and sociology. It focuses on patterns of relations among individuals and among groups such as states and businesses. Social network analysis has been extensively used to understand behavior between individuals or organizations through their linkages on social media platforms such as Facebook and Twitter.

It is also used in intelligence and law enforcement activities. The National Security Agency, for example, exploits its electronic surveillance systems to produce data required to conduct this kind of analysis on networks associated with national security such as terrorist cells. Businesses and private organizations use social network analysis to support activities like information system development analysis, customer behavior analysis, and business intelligence needs.

Varun Kumar is a professional science and technology journalist and a big fan of AI, machines, and space exploration. To find out about his latest projects, feel free to directly email him at [email protected]. Commetrix Best for : Analyzing dynamic network change and lifecycles Commetrix is a dynamic network visualization and analysis software that provides easy exploratory yet comprehensive access to network data. Key Features Renders 2D and 3D networks of communication Allows for time-based observation of growth of the network Splits network into groups based on the network structure Overall, Commetrix allows you to observe the detailed lifecycle of a communication network of thousands of concurrently changing relationships.

Social Network Visualizer Best for : Analyzing graphs and social network characteristics, and creating HTML reports This user-friendly, cross-platform tool allows you to draw social networks on a virtual canvas, load field data from a file, or browse the Internet to create a network of connected webpages. Key Features Advanced metrics for social network analysis such as centrality and prestige indices Fast algorithms for community detection Structural equivalence analysis Social Network Visualizer calculates standard graph and network cohesion metrics like density, eccentricity, clustering coefficient, etc.

Key Features Annotate graphs, entities, and relations with metadata Filtering mechanism lets you focus on specific portions of a graph Allows you to analyze large sparse graphs with more than , nodes JUNG supports the implementation of numerous algorithms, including random graph generation, clustering, optimization, network distance calculation, and statistical analysis.

Tulip Best for : Visualizing semantic networks Tulip is a lightweight framework for analyzing and visualizing relational data. Key Features Domain-specific visualizations Graph clustering Visual attribute mapping The framework supports a wide range of algorithms for graph visualization, including graph drawing algorithms, metrics algorithms, clustering algorithms, and visual attribute mapping algorithms. Statnet Best for : Implementing recent advances in network modeling based on ERGM Statnet is a suite of R packages that perform a wide range of data management, visualization, and statistical network analysis tasks.

Key Features Optimized for speed and robustness Provides a unifying coherent framework for modeling Provide a complete stochastic representation of the process of network formation The broad functionality of Statnet is powered by a central Markov chain Monte Carlo algorithm that can easily handle networks having several thousand nodes.

Netlytic Best for: Analyzing online interaction with large online communities Netlytic is a cloud-based social network analyzer that is capable of automatically summarizing textual data and discovering communication networks from publicly accessible social media posts.

A hub is a node that has many edges pointing out of it. An authority, on the other hand, is a node that has many edges pointing to it. You can also think of authority as a node that is the ending node of many edges. In addition, remember that not all nodes in a directed network will be a hub or an authority. Dyads and cliques are pairings of nodes connected by edges. A dyad is a pairing of two nodes, while a clique is a pairing of three or more nodes. While a dyad or clique may be a connected component, they can also be part of a larger connected component.

Now that you have an understanding of social network analysis terms and concepts, this guide will walk you through applying these techniques to a data set using Gephi. First, download and install the Gephi software for the operating system your machine is running. Gephi is available for Mac, PC, and Linux.

While this data set is already laid out with a node and edge list, when working with data sets not structured as a network this will require some data transformation skills.

I recommend using Python and Pandas in these situations. After downloading the data set, there will be three csv files: nodes, edges, and network. Open the file nodes. The nodes file contains a list of all the nodes in the network. This file has two columns: node and type. This network contains two different types of nodes that represent different actor types: heroes and comics.

The edges file also contains two columns: hero and comic. Each row in this table represents a single edge. The hero node and comic node are the two nodes connected by the edge. However, in a directed network the source column contains the starting node and the target column contains the ending node.

Now that the node and edge lists are properly formatted for Gephi, it is time to load the data. Click on new project. To import a list click the import spreadsheet button. Then navigate to the folder containing the data sets and open the nodes file.

An import wizard will then walk you through correctly importing the node list. Set separator to comma, import as to nodes table, and charset as UTF Then click next. After clicking next, the wizard will provide additional setting configurations.

Set time representation to intervals. For imported columns, check the node and type boxes and set their data types to string. Then, click finish. There is one more step in importing the nodes list. Set graph type to undirected and edges merge strategy to sum. Then, click OK. You should now see some data in the data laboratory window!

Next we need to import the edges list. Click on the overview tab. You might be disappointed in the graph that was visualized. It will likely look like the black mess below. Select this function and then click run. After running the layout function your graph should look something like the one below. You can continue to play with other layout functions if you wish to get a better node position. In addition, you can change the parameters of layout functions. The network size is easy to find.

In the upper right-hand corner is a pane called context. This window provides the number of nodes and edges in the graph. Click on the statistics tab. The statistics window contains many measures that can be calculated on the network.

To find the network density, click run for graph density. A new window will pop up showing the results. This Marvel network has a density of 0. You can save this report by clicking the save button in the bottom left-hand corner, or close it by clicking the close button in the bottom right-hand corner.

Recall that centrality measures are on a node-level, and not a network-level. However, we can also average centrality measures to get a network-level metric.

In Gephi, you calculate centrality measures as a network-level average, which then also inputs the centrality measure on a node-level into the data laboratory tab. To calculate node degree, click run on the average degree algorithm in the statistics window. The report will provide you with the average degree for the network, as well as a distribution graph. While these can be useful in some applications, we are more interested in the degree on a node-level.

Close the report. To see the degree for each node in the network, go back to the data laboratory window and click on the node table. You will see a new column in the data titled degree. Calculating node closeness and betweenness is a similar process as calculating node degree. In the statistics window, click run on the network diameter algorithm. Select undirected and click OK. Depending on the specs of your machine this may take a little while to calculate.

Like with the node measure, Gephi will provide a network-level report. Click close on this report and go to the data laboratory. Edge weights are auto-calculated in Gephi. You can find them in the edge list within the data laboratory. Currently, our graph nodes and edges are black, providing no additional information. You can color-code both nodes and edges in Gephi. The coloring options are in the appearance window. To color-code the nodes of the graph based on the node degree, click on the nodes button and the color palette button in the appearance window.

There are three options to encode information in the color of nodes: unique, partition and ranking. Partition will break the nodes into color-coded groups. Ranking will color-code the nodes on a scale. Let's color the nodes by their degree. To do this, click on the ranking section and select degree. A color scale will be used to color the nodes. Naturally, the human eye is drawn to colors and patterns.

In this post, I reviewed the best data visualization tools to interpret customer data and connect it to your backend database. These reviews are based on ease of use, drag-and-drop dashboard styles, integration capabilities, pricing, and more. Disclaimer: This article contains affiliate links that I may receive a small commission for at no cost to you if you purchase a plan.

However, these are the tools I have vetted and fully recommend when it comes to visualizing your business data. You can read my full affiliate disclosure in my privacy policy. Here are my top picks for the best data visualization tools and platforms to use this year. Zoho Analytics is probably one of the most popular BI tools on this list. One thing you can stay assured of is that with Zoho analytics, you can upload your data securely.

Moreover, you can use a wide variety of charts, tables, and components to transform your data into a concise manner. You also get a day free trial to check out the product. Get started with Zoho Analytics. Databox is a data visualization tool used by over 15, businesses and marketing agencies. Databox pulls your data into one place to track real-time performance with engaging visuals.

Databox is best for marketing teams that want to quickly get set up with dashboards. Databox has a free plan that allows up to 3 users, 3 data sources, and data refreshed daily.

Get started with Databox and try it for free. If you want to see and visualize data easily, then Tableau is the data visualization tool for you. It helps you in creating charts, maps, and every other type of professional graphics.

To enhance your visual representations, you can find the desktop application as well. Tableau is most suitable for corporate companies who are looking for data visualization solutions without having to set up manually. Infogram is a web-based data visualization and infographics platform. It permits you to make and share infographics, maps, and charts by taking all the user data and converting it into compelling graphics. After that, you can further share, embed, or publish those graphics wherever you want.

Moreover, the tool is effortless to use and is suitable for government bodies, marketing teams, and even students. Infogram is most suitable for organizations that work with sales and marketing since it allows you to display your targets with the help of charts and templates.

Infogram also offers a freemium plan for essential features. ChartBlocks is an innovative data visualization software that enables you to import your data within a fraction of seconds. Moreover, you can directly update your data right away in the ChartBlock app and do the necessary import. It has a certain high level of tools. After this, you can easily embed the charts to your website or any other social media.

The first plan is entirely free of cost, where you get up to 50 active charts. Datawrapper is another excellent data visualization software on this list. Your target audience should be able to understand whatever type of chart you create. And Datawrapper solves this issue automatically. All the charts, tables, and maps you create with DataWrapper are easily readable on all devices. The free plan also allows you to export your chart, tables, and maps into png format.

Plotly not only helps you with creating graphics, but it also provides in-depth analytical reports to help you keep track of the data. It also comes with an easily customizable user interface. On top of that, it helps you export the reports easily and works superbly well in the business intelligence field.

If you require basic features, then you can subscribe to the freemium plan. Visually is a community platform for data visualization and infographics.

It permits users to search for various images through tags and descriptions. You can also publish and embed graphics directly to your social network profiles. It works in three simple steps. The pricing depends upon the number of users. Also, the tool is open to use but for a limited duration. It supports declarative programming and helps you manipulate documents based on data. Other than this, it provides unique functions, such as code reusability, a wide variety of curve generating functions, help in associating data to an element in the HTML pages, and more.

It is best suited for those firms that need to create web pages and websites. You can also build excellent apps with the help of this data visualization tool.