The value of graph analysis
What is graph analysis
Graph analysis allows us to examine the world from the perspective of
a network made up of entities (nodes) and relationships (links) between them, to better
understand many social and natural phenomena encountered in everyday life.
“ To explain or infer the results of reality, it is not enough to analyze the properties of participating entities,
and their relational characteristics must be considered together. ”
To gain insight based on a data analysis, you need to understand the relationships between data and their context.
Graph analysis is an optimal way to analyze relationships in the context of other relationships, extending the scope of perception
from entities to relationships, and from groups to networks.
Scalability of graph analysis (social network analysis)
Graph analysis can be applied to practically all phenomena which may be seen from the perspective of entities (nodes) and their
relationships (links)– that is, networks, in fields such as finance, technology, bio, literature management, and healthcare, as well as social networks.
This method is universally used for complex system network analysis, network science, and social big data analysis.
NetMiner, Graph Data Science software
Uniqueness of NetMiner
GUI optimized for graph analysis
that does not require programming
NetMiner's intuitive interface allows the users to promptly
and conveniently learn graph analysis.
For users unfamiliar with graph analysis, NetMiner guides you through
the graph analytical process.
NetMiner users can derive graph analytical results with just a click, without
any programming knowledge.
NetMiner provides users with an intuitive GUI that follows a standardized analysis
pipeline. This means that even beginners in graph analysis can walk through the
analysis cycle consisting of data input, preprocessing, analysis, and visualization.
In addition, NetMiner provides quantitative analytical results and graphical results
at the same time to ensure that the users can intuitively understand, reanalyze, or
explore the analytical results.
Support for convergence analysis for graph data
NetMiner provides optimal functions to analyze the graph data by
comprehensively utilizing graph analysis, machine learning, and statistical
NetMiner provides various graph data processing methods and visualizations,
algorithms, and over 100 graph analysis algorithms.
In addition, it supports functions such as natural language processing for text network
analysis, text network creation, and topic analysis, and also provides statistical analysis
and machine learning algorithms for graph data. Users can produce more accurate
analytical results by combining the results of various analysis algorithms and adopting
the appropriate analysis algorithm according to the users’ field of interest.
Combination of graph analysis
and machine learning
NetMiner can help users solve problems by combining graph analysis
and machine learning.
Today, the need to analyze graph data consisting of nodes and links rather than
simple tabular data to solve complex business problems is growing. Furthermore,
graph analysis technology can help improve the accuracy of analysis because it
can additionally utilize relational context information.
With NetMiner's graph analysis algorithm, users can gain a new problem solving
method for their business or research. As well, the results of graph analysis can
be used as the input data for machine learning and statistical analysis to help
improve the analytical accuracy and gain additional insights.
Key Features of NetMiner
Over 10,000 users around the globe rely on NetMiner.