NetMiner
 | This article may require copy editing for formatting and tone. (November 2012) |
NetMiner is an application software for exploratory analysis and visualization of large network data based on SNA(Social Network Analysis). 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 in 2001 as a commercial analysis software speicalized in social network analysis. There are various license not only for commercial use, but also for non-commercial academic use.[2] In addition, NetMiner 4 license for coursework is provided to students and teachers for free if answering a questionnaire. The current version is 4 for Microsoft Windows (2000 or later version).[3]
 | |
 | |
| Developer(s) | Cyram Inc. |
|---|---|
| Initial release | December 21, 2001 |
| Stable release | 4
/ July 27, 2011 |
| Written in | Java |
| Operating system | Windows |
| Available in | English |
| Type | Social Network Analysis / Visualization |
| Website | www |
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 their own customized plugin 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 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.
Attribute of Node and Link
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 users 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 | Network[# of Links, Density, Average Degree, # of Components(Weak/Strong),
Inclusiveness, Reciprocity(Arc/Dyad), Transitivity, Clustering Coefficient, Mean Distance, Diameter, Connectivity(Node/Link), Connectedness, Efficiency, Hierarchy, LUB], 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 users to generate a programming script automatically. Then users 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 libraries for mathematics and statistics written by Python can be applicable within NetMiner 4 without any modifications, and ordinary data structures which whar provided by Python can be defined. Users 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. It requires users to learn the language. NetMiner Script is based on Python which is a programming language distributed for free and widely used. Python's syntax is well known to be quite intuitive and easy to pick up for new programmers. [5][6] Also, there are many libraries for mathematics or statistics written by Python and these libraries can be imported to NetMiner 4 and used with modules in NetMiner 4 without any additional programming. Using loops, conditionals, the in-depth analysis is available. And users can create a batch file which is executed automatically for NetMiner.
WYSIWYG Scripting
NetMiner provides user-friendly enviroment with GUI(Graphic User Interface) and NetMiner Script is a GUI-based script. It means all tasks executed through GUI can be controlled and expressed by script language. Modules in NetMiner 4 are executed in identical ways regardless of the interface. In addition, NetMiner 4 provides Script Generator which generates the script code automatically by clicking items on GUI which is the same as GUI of NetMiner.
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
See also
References
- ^ Furht, Borko (2010). Handbook of Social Network Technologies and Applications. Springer Press. p. 19. ISBN 978-1-4419-7141-8.
- ^ NetMiner website(www.netminer.com) > License
- ^ NetMiner website(www.netminer.com) > System Requirements
- ^ NetMiner website (www.netminer.com) > Main features of NetMiner
- ^ Bloom, Brian (26 October 2012). "Python simplifies big data". ComputerWorld Canada. Retrieved 31 October 2012.
- ^ Jackson, Joab (30 October 2012). "Python - Big Data's secret power tool". IDG News Service. Retrieved 31 October 2012.