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NetMiner

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NetMiner is an innovative software tool for exploratory analysis and visualization of large network data. It can be used for general research and teaching in social networks. This tool allows researchers to explore their network data visually and interactively, helps them to detect underlying patterns and structurres of the network.[1] It features data transformation, network analysis, statistics, visualization of network data, chart, and a programming language based on the Python script language. It has been released 2001 as a commercial analysis software speicalized in social network analysis. There are various license not only for commercial market, but also for non-commercial academic user.[2] The current version is 4 for Microsoft Windows[3].

NetMiner
Developer(s)Cyram Inc.
Initial releaseDecember 21, 2001 (2001-12-21)
Stable release
4 / July 27, 2011; 13 years ago (2011-07-27)
Written inJava
Operating systemWindows
Available inEnglish
TypeSocial Network Analysis / Visualization
Websitewww.netminer.com


Key Features

NetMiner has 8 main features.[4]

  • Expressive Network Data Model: NetMiner has data structure that can express various types of nodes, links, node attributes and link attributes. In this flexible data structure, almost any social events and research topics can be modelled for network analysis.
  • Large Scale Network Data Processing: With proper license, computing power, and some algorithmic limitation, NetMiner can handle over 1,000,000 nodes and 10,000,000 links.
  • Comprehensive Network Measures and Methods: In NetMiner, about 140 analysis modules are mounted. There are 26 modules for data transformation, 66 modules for network analysis, 19 modules for statistical analysis, and 37 modules for network visualization and chart.
  • Standardized Option and Output Process: All of the analysis sessions in NetMiner are composed of five common analysis components that support standardized analysis process: input, pre-process, main-process, post-process, and output.
  • Intuitive User Interface: NetMiner is driven by GUI and provides intuitive workflow management interface for complicated analysis.
  • NetMiner Script Environment: NetMiner provides a python-based script environment. This can access most functions of NetMiner including data management, network analysis, and visualization. Users can create his or her own custom plugins by NetMiner script.
  • Mix and Match of Numerical and Graphical Output: Exploratory analysis based on the results is available on the graphical output panel. With this visual exploration, intuitive and dynamic interpretation of network analysis results is available.
  • Integrated Environment for Statistical Analysis: NetMiner provides built-in methods for statistical analysis and visualization.


Screenshots

File:Netminer4 main window capture 006.jpg
Data import
Data information
Styling & Network Map Explore
Matrix Diagram
Contour Chart
Script Workbench

File Formats

NetMiner data file format

  • .NMF

Importable/Exportable formats

  • Plain text data: .TXT, .CSV
  • MS-Excel data: .XLS, .XLSX
  • NetMiner 2 data: .NTF
  • UCINet data: .DL, .DAT
  • Pajek data: .NET, .VEC, .CLU, .PER
  • StOCNET data file: .DAT
  • Graph Modelling Language data: .GML(importing only)


Data Structure

Overview: Hierarchy of NetMiner Data Structure

A DataSet, composed of data items, is a basic unit in NetMiner. A DataSet is used as an input data for all the analysis and visualization Modules in NetMiner. A DataSet is composed of four types of data items: Main Nodeset, Sub Nodeset, 1-mode Network data and 2-mode Network data. A DataSet can have only one Main Nodeset. But multiple 1-mode Network data can be contained in a DataSet. Also a DataSet contains multiple Sub Nodesets and multiple 2-mode Network data. ProcessLogs which are generated by analysis and visualization process can be managed with the DataSet in a Workfile. A Project contains independent multiple Workfiles. A number of nodes in Main NodeSet of each workfile does not need to be the same. In this way, the hierarchy of NetMiner data structure is as follow:

Project > Workfile > Dataset > Data items

Data Item

Nodeset

Nodeset refers to a group of nodes which have common characteristics.

Network Data

Network data in NetMiner refers to the data which represents a relationships between nodes. It can be formd as a matrix or a link list. A relationship between nodes in one nodeset is allowed only for a main nodeset, and called 1-mode network. A relationship between nodes in more than two nodesets can be defined as a relationship between main nodeset and sub nodeset and is called 2-mode network.

Attributes can be defined for node or link in NetMiner. Attributes can be as numbers, text, datetime, time, and have missing value. The attribute is used for network data analysis or visualization. Specific modules of NetMiner require attributes as one of input data. Using the attribute of node or link, it is available to extract some sub network from whole network data. Also it is used for styling network maps.

Dataset

Multiple Sub Nodeset

A dataset can contain multiple nodesets. In NetMiner, nodesets are divided into two types: main nodeset and sub nodesets. Only a single nodeset can be defined as a main nodeset and the rest is defined as sub nodesets in a dataset.

Multiple Network Data (Layer)

A dataset can include multiple network data. 1-mode network data refers to relationship between nodes in a main nodeset. More than two seperate network data can be defined under a main nodeset. For example, there are six nodes {A, B, C, D, E, F} in a main nodeset, more than two network data named "love", "hate" can be defined as follow,

  • 1-mode Network 1 (Love) = {(A,B), (A,C), (B,D), (C,E)}
  • 1-mode Network 2 (Hate) = {(A,C), (B,C), (D,E), (A,E)}

A group of networks under a nodeset is called a network layer in NetMiner. There can be 1-mode network layers under a main nodeset, and 2-mode network layers under a main nodeset and a specific sub nodesets. Multiple network data(layers) in a nodeset can be integrated in analysis and visualization process.

Workfile

Workfiles are composed of a dataset and a process log. NetMiner uses a concept of a workfile to manage a dataset and module process simultaneously. A process log shows traces of process execution, and is composed of Session, QuerySets, and Selections. When a module is opened, a session is created and added to a list. And when a query or a selection is saved, it is added to the list.

Project

A NetMiner data file (.NMF) corresponds to a single project. In a project, there can be more than one workfile. There are two reasons for grouping multiple workfiles in a single project file. A new workfile is created when any changes of composition of nodes occur in a main nodeset by intentional control or executed process. Workfiles can be managed in a tree structure. So user can track history of data changes using the workfile tree.


Modules in NetMiner

Data Transformation

Category Module
Value Dichotomize, Reverse, Normalize, Recode, Missing, Diagonal
NodeSet Ego Network, Reorder
LinkSet Incidence, Line Graph, Link Reduction
Matrix Vectorize (1-mode network), vectorize(2-mode network)
Layer Split, Merge, Multiplex
Mode 2-mode network, 1-mode network, Main Node Attribute, Tree Construction
Random Erdos-Renyi, Scale-Free, QAP Permutation, MCMC

Network Analysis

Category Module
Neighbor Degree(Neighbor), Ego Networks, Structural Hole, Homophily, Assortativity, Equicentrality
Subgraph Dyad Census, Triad Census, Triad Combination
Connection Shortest Path, All Path Finding, Dependency, Node Connectivity, Link Connectivity,

Maximum Flows, Pfnet, Minimum Cutset, Influence, Accessibility

Cohesion Component, Bi-Component, Clique, n-Clique, n-Clan, k-Plex, k-Core, Lambda Set,

Community, Betweenness, Modularity, Eigenvector, Label Propagation, Cohesive Block, s-Clique

Centrality Degree, Coreness, Closeness, Node Betweenness, Link Betweenness, Flow Betweenness,

Eigenvector, Status, Power, R.W.Betweenness, Information, Load, Effects, PageRank, HITS, Community

Equivalence Structural(Profile, CONCOR), Regular(REGGE, CatRE), Role(Triad, Local), Sim Rank
Position Blockmodel(Conventional), Brokerage, Bow-Tie Model
Properties Multiple Network, Group, Modularity
Models Dyadic Interaction(P1), ERGM(P), Blockmodel(Generalized), Influence Network(Effects, Sequence)
Two Mode Degree, Eigenvector Centrality, Collaborative Filtering, Maximum Matching

Statistics

Category Module
Basic Frequency, Gini Coefficient, Power Law, Descriptives, Crosstabs, ANOVA,

Correlation, Autocorrelation, Regression, Logistic Regression

Multivariate Analysis MDS, Correspondence, Cluster, Decomposition(Eigenvector, Singular, Spectral),

Covariance Matrix, Principal Component, Factor Analysis

Network Visualization

Category Module
Spring Layout

(2D/3D, 1-mode/2-mode)

Kamada & Kawai, Stress Majorization, Eades, Fruchterman & Reingold,

GEM, HDE(High-Dimensional Embedding)

MDS (2D/3D) Classical MDS, Non-metric MDS, Kn-MDS
Clustered Layouts (2D/3D) Clustered Eades, Clustered-CoLa
Layered Layout (2D) Dig-Cola
Circular Laytouts (2D) Circumference, Concentric, Radial
Others (2D) Fixed, Random

Charts

Category Module
Chart Pie Chart, Matrix Diagram, Area Bar, Box Plot, Scatter Plot,

Contour Plot, Surface Plot, Network Contour Plot, Network Surface Plot


Script Workbench in NetMiner 4

NetMiner 4 equips Script Workbench based on Python script language with Script Generator which enbles user to generate a programming script automatically. Then user can operate functions in NetMiner 4 by using GUI or programmable script lanague. Most features of NetMiner can be performed using a script rather than clicking a menu so that complicated series of commands can be stored in scripts and executed repeatedly without additional work. Analysis flows, such as loops and conditional branching can be controlled by a user in a flexible way. Also, various existing mathmatics and statistics libraries written by Python can be applicable within NetMiner 4 without any modifications, and ordinary data structures which whar provided by Python can be defined. User can develop their own algorithms by combinations of NetMiner features. In addition, a generated script file can be added to NetMiner 4 as a one of menu by a form of plug-in which can be shared with other NetMiner users as well.

Features of NetMiner Script

Python-based scripting

Most analysis software packages with scripting feature use their own scripting language. This requires the user to learn the language, which limits the user base and consequently it’s not easy to find related material either. NetMiner Script uses Python, a programming language distributed free of charge and widely used all over the world. Non-computer majors often get cold feet when they hear the word programming language, but Python is especially so easy that even complete beginners who have little knowledge of programming can learn and use it without difficulties, provided they put in some effort. Also, Python has a wide user base, which means there are lots of easily-obtainable materials available on the Web for learning Python. To use analysis algorithms involving math, statistics or data mining for example, you go to a place on the Web where Python codes written by other people are shared and then, you can download and use the pertinent source codes. This is especially helpful for Python beginners. Therefore, learning NetMiner Script is not simply learning another script language ? it is also a gateway to help you to enter the vast world of Python.

WYSIWYG scripting

NetMiner is a GUI program and NetMiner Script is a GUI-based script, which means all tasks executed through the GUI can be controlled by the use of scripts. Although NetMiner is not the only analysis software that uses both GUI and script, it does differentiate itself from others in the following respects: Generally, in the case of analysis software that uses both of the interfaces, although the results of execution in GUI mode and scripting mode are the same, their processes are not exactly the same. In most of the analysis programs, scripts control data and processes in a way unrelated to the GUI. In contrast, the way of writing NetMiner scripts, is the same. Therefore, while writing a NetMiner Script, the user can see in their mind’s eye which task is corresponding to the execution in GUI. To make it more convenient in generating scripts, NetMiner enables users to utilize the script generator in the Script Workbench. By using it, scripts can be automatically generated according to items selected through the GUI, which helps to avoid the hassle of manual coding in each process. Because of this, NetMiner Script can be called a WYSIWYG (What You See Is What You Get) way of scripting.


Release History

The first version of NetMiner was released on Dec 21, 2001. And there have been four major updates from 2001 to 2012.

NetMiner

Released on December 21, 2001.

  • Network analysis modules and network visualization modules were integrated to one package
  • User interface for data analysis and management was added
  • Generic data structure for multi-layer network was introduced

NetMiner 2

Released on April 9, 2003.

  • Modules for importing external data were introduced
  • Some measures and methods for network analyses and statistical analyses are added and improved
  • Some algorithms for visualization processes are added and improved

NetMiner 3

Released on May 15, 2007.

  • Data structure was improved for huge network analysis
  • Analysis and visualization modules are integrated to support standardized analysis processes
  • Data import modules to access external DB such as Oracle and MS SQL were introduced
  • Environments for visualization and analysis were integrated to one

NetMiner 4

Released on May 10, 2011.

  • Python-based NetMiner Script was introduced
  • Encryption module for NMF format was added
  • Current(2012.6.25) version: 4.0.1.f.111222


About Cyram Inc.

Cyram Inc. is located in Seoul, Korea. Company mission is to deliver customers analytic value based on social network analysis methodology through solutions and consulting services. more



References