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Latest Social Network Analysis Software

  • Soumyaa Rawat
  • Aug 14, 2021
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What is Social Network Analysis (SNA)?

 

Social Network Analysis is the method of studying social relationships between people, computers, organizations, and other related elements. A collection of models and tools provides a graphical, visual, and mathematical representation of human relationships. 

 

In order to understand online interactions, social network analysis in data mining brings a closer look towards data analysis in relation to group communications. Also known as network science, social networks analysis is a branch of data analytics that analyzes networks and online communication in order to understand social structures. 

 

Consisting of nodes, networks are represented by ties between different nodes (entities - individuals, computers, etc.) that define the way social groups operate. The increasing need for sociologists to discover social structures and the way people in a network interact has led to the emergence of social network analysis. 

 

While mathematical analysis is used to understand social relationships in a quantitative manner, the data visualization technique in SNA is used to understand the pictorial connection between the different nodes or entities in a network. 

 

Herein, the flow of interaction between 2 nodes is understood in the light of directional or non-directional relationships. 

 

Another highlight of such an analysis is that this network analysis of group behavior and individual behavior can be used as a learning analytics tool when it comes to online problem-based learning. That said, social network analysis has a number of applications in various sectors. 

 

(Suggested read: What is social media marketing?)

 

 

Applications of Software Network Analysis

 

In this segment, we will understand the use of Social Network Analysis (SNA) in various sectors. In short, let us discover the applications of SNA in contemporary times.  

 

  1. Data Science

 

The first and foremost application of SNA is data science. In order to understand the flow of data from one node to another in a network setting, SNA helps one to understand the relationship between 2 nodes in a network. 

 

Moreover, the concept also helps the researchers to understand the way data influences the masses in a group and what influences them to look for a particular data set. 

 

That said, social network analysis, although primarily meant for sociology, is useful in other realms too. A field of data analytics itself, SNA is surely one of the most important contributors for analyzing the desire for data. 

 

  1. Marketing

 

Another field in which SNA is widely used is marketing. Focused on promoting a brand of a product, marketing professionals of a company often rely on SNA for launching marketing campaigns. 

 

As SNA helps them to understand the way social relationships are structured, they focus on products that are most desired by the masses and tend to narrow down their marketing stints around such brand identities only. 

 

Moreover, digital marketing also benefits from the concept. As SNA also helps to identify the initiators and receivers (directional relationship) in a social structure, marketing professionals approach influencers (celebrities and role models) to promote their product which further leads them to influence the masses. 

 

(Also read - Role of Big Data in Content Marketing

 

  1. Fiscal Frauds

 

As SNA is a branch of data analytics, it directly targets the data that is available at large. That said, SNA software detects fraud and risk management by detecting anomalies or abnormal merchant-buyer patterns while closely eyeing the way members in such a network operate. 

 

While some might think that keeping a check on different nodes of a network is a heterogeneous task incapable of detecting frauds, it can surely identify the abnormal activities going on in a network, leading to the detection of fiscal frauds. 


 

Top 10 SNA Applications/Software 

 

As we have learned about social network analysis, we will now come to know about the top 10 Social Network Analysis applications (or software) that have made the task of conducting social network analysis possible. 

 

Such social network analysis software has not only made the process more efficient, but also accommodates ML models and social network analysis tools to understand social structures in a better manner. 

 

  1. JUNG

 

Java Universal Network/Graph Framework or simply put, JAVA is an open-source library or a network analysis software that renders the programming language for software network analysis, data manipulation, and data visualization in the form of a network or graph. 

 

One of the top 10 software network analysis tools to work with in 2021, JUNG is written in Java language. The current features of the software include data mining, SNA, optimization, statistical analysis, and graph theory.  

 

 

  1. UCINET

 

A sought-after software for social network analysis that comes with the NetDraw Program to draw social network diagrams. A comprehensive package that can quantitatively analyze social networks, Ucinet has built-in SNA tools that can dig deep into the data structure. 

 

While usual SNA applications can handle up to 5,000 - 10,000 nodes, Ucinet can very well handle up to 32,767 nodes with optimal speed.  

 

 

  1. TULIP

 

A software loaded with data visualization tools and techniques, Tulip is an information visualization framework that draws data from social networks organized in the form of links between various nodes or entities. 

 

Written in C++, this interface is capable of visual encodings, data models, and social network analysis. 

 

“One of the goal of Tulip is to ease 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.” SNA by TULIP 

 

 

  1. NodeXL

 

Launched in 2013, NodeXL is another social network analysis software that is hassle-free and comes with a variety of network analysis tools - generic graph data, visual representation of networks, and advanced network analysis. 

 

Similar to Microsoft Excel, this software uses a familiar template for users to have an interactive yet efficient experience. 

 

From sociology to information visualization, to computer analysis, this software has numerous applications in today’s scenario. 

 

(Must read: Network graph and network topology)

 

 

  1. NETLYTIC

 

Defined as a social media text and social media analyzer, Netlytic is a software network analysis application. A cloud-based application, Netlytic is capable of summarizing textual data and analyzing communication networks. 

 

Used by researchers, sociologists, and students, its API enables it to filter information from social media platforms to help analysts understand the meaning of text datasets. 

 

A free data analysis software for all to use, Netlytic renders both data visualization and data analysis. 

 

 

  1. GEPHI

 

To dig deep into network graphs, one needs software that supports the cause. Gephi is an SNA software that does exactly that. Apart from social network analysis, this software also performs exploratory data analysis, link analysis, and biological network analysis. 

 

With interactive and hassle-free features like real-time visualization and cartography, this application is a must-have for every individual who is looking to perform social network analysis. 

 

 

  1. SOCNETV

 

Known as Social Network Visualizer, Socnetv accommodates advanced tools for social network analysis. With a built-in web crawler to create social networks and perform comprehensive documentation, this application aims to create meaningful visualizations. 

 

With data visualization and network analysis, one can extract intuitive information from directed/undirected graphs to detect connectedness in a social network.  

 

(Must read: What is knowledge graph?)

 

 

  1. GRAPHVIZ

 

An open-source graph visualization application. Graphviz is another leading SNA application. An application that enables the user to extract a visual representation of abstract graphs and network diagrams, Graphviz can be used in various fields like biometrics, sociology, machine learning, and web design. 

 

Even though the present times have access to vast amounts of data, Graphviz accommodates the technique of Exploratory Network Analysis (ENA) to give way to data visualization and manipulation. (Given)

 

 

  1. NETMINER

 

A Python-based script, Netminer is a social network analysis tool that comes with a set of enhanced user interfaces and interactive functions like 3D Network Mapping, visual data exploration, and data mining. 

 

All in all, it accommodates the optimal quality tools that are equipped with the latest ML models and algorithms

 

That said, it is undoubtedly one of the leading SNA software that comes in handy for first-time users as well as experienced professionals. 

 

 

  1. NETWORKX

 

A Python library for social network analysis, NetworkX is a leading SNA software that is reliable and optimal for data mining and related processes. 

 

A comprehensive package that provides data creation, manipulation, and structure analysis, NetworkX is an easy-to-use and multi-platform interface that can be used by both beginners and experienced professionals. 

 

 

Conclusion

 

To sum up, SNA is a concept used to study social relationships in a network consisting of nodes (various entities like individuals, computers, etc.). Even though the concept takes networks into consideration, it is capable of analyzing group and individual behavior. 

 

A number of SNA software have made the job easier. This has, in turn, broadened the scope of social network analysis in the coming years. Some of these applications are Netminer, Graphviz, and NetworkX. 

 

(Recommended blog: Top neural network programs/software)

 

With salient features like mathematical and visual analysis, these applications render an in-depth knowledge of how different nodes in a network share data and co-exist in the world of information. 

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