Graph Data Science Platform

NetMiner365

You can gain new insights into complex interconnected phenomena in society, economy, and physical phenomena through graph analysis and social network analysis.

NetMiner365 (CYRAM)

With NetMiner365, you can easily explore graph structures in big data, apply graph analysis and machine learning techniques to uncover hidden relationships, and build predictive models anytime, anywhere.

— Overview

Cloud-based online service(SaaS) platform for graph data science

NetMiner365 allows anyone to easily extract graph structures from big data and comprehensively utilize graph analysis and visualization, statistics and machine learning, graph machine learning, and more. Analyze big data in a differentiated way without constraints of location and time in a cloud environment.

Features

Integration of Graph Analytics and Machine Learning

By leveraging graph analysis, the obtained contextual relationship information can be utilized in machine learning to enhance the accuracy of predictions.

Graph Analysis with Optimized GUI

With an intuitive interface, you can analyze and visualize graphs easily without the need for complex programming codes.

Support for Convergent Analysis of Graph Data

In addition to various graph data processing and analysis algorithms, the platform provides statistics and machine learning capabilities, enabling convergent analysis to obtain differentiated results.

Cloud-Based Online Service

With web-based access anytime, anywhere, you can reduce the constraints of system and hardware, saving time and costs required for big data analysis.

Function

01 Data Management

You can quickly upload large volumes of data stored in databases or files and easily extract and structure relational data from big data. Additionally, various preprocessing functions tailored for graph data are provided. For example, query functions that allow data extraction and filtering based on graph-based conditions are suitable tools for big data processing.

02 Graph Analysis

Comprehensive graph analysis (social network analysis) capabilities enable measuring the centrality of nodes (entities) and performing clustering (cohesive groups, equivalence clusters) based on links (relationships) between nodes.

03 Graph Machine Learning, General Machine Learning, and Deep Learning

The platform offers graph machine learning, machine learning, and deep learning techniques that enable data classification and model building. It includes graph machine learning algorithms such as GCN and Node2Vec for learning graph data, general machine learning algorithms such as Naïve Bayes, SVM, CART, k-means, and deep learning algorithms (MLP), as well as statistical functionalities.

04 Data Visualization

You can intuitively understand data through various charts. We provide not only basic charts but also complex ones like Parallel Coordinates and Treemap for intricate data. Additionally, we offer graph visualization to visually comprehend the structure of graphs. Through styling of graph visualization, you can represent the necessary information in graph images.