1. Class 3: Introduction to CINET
We are surrounded by systems that are hopelessly complex, from the society, a collection of seven billion individuals, to communications systems, integrating billions of devices, from computers to cell phones. Our very existence is rooted in the ability of thousands of genes to work together in a seamless fashion; our thoughts, reasoning, and our ability to comprehend the world surrounding us is hidden in the coherent activity of billions of neurons in our brain. Many of these systems appear to random to the casual observer, but upon closer inspection they are found to display endless signatures of order and self-organization. These systems are collectively called complex systems, and the research area that aims to explore and understand them is often referred to as complexity. Given the important role they play in our life, their understanding, quantification, prediction and eventually control is the major intellectual scientific challenges of the 21st century.
Class 3: Introduction to CINET
Tools for network analysis and visualization
Prof. Boleslaw K. Szymanski
Konstantin Kuzmin
Network Science: Introduction to CINET 2015

2. Tools for network analysis and visualization
TOOLS OVERVIEW (LISTED ALPHABETICALLY)
Tools for network analysis and visualization
Computing model and interface
Desktop GUI applications
API/code libraries, Web services
Web GUI front-ends (cloud, distributed, HPC)
Extensibility model
Only by the original developers
By other users/developers (add-ins, modules, additional packages, etc.)
Source availability model
Open-source
Closed-source
Business model
Free of charge
Commercial
Network Science: Introduction to CINET 2015

3. CyberInfrastructure for NETwork science
TOOLS CINET
CyberInfrastructure for NETwork science
Accessed via a Web-based portal
Supported by grants, no charge for end users
Aims to provide researchers, analysts, and educators interested in Network Science with an easy-to-use cyber-environment that is accessible from their desktop and integrates into their daily work
Users can contribute new networks, data, algorithms, hardware, and research results
Primarily for research, teaching, and collaboration
No programming experience is required
Network Science: Introduction to CINET 2015

4. Network Data Integration, Analysis, and Visualization
TOOLS Cytoscape
Network Data Integration, Analysis, and Visualization
A standalone GUI application
A platform for visualizing complex networks and integrating these with any type of attribute data
Originally developed for biological research
Includes features for data integration, analysis, and visualization
A variety of layout algorithms, including cyclic, tree, force-directed, edge-weight, and yFiles Organic layouts
Implemented in Java
Runs on any Java-supported platform
Modular architecture extensible through plugins (called Apps)
Open-source and free of charge
Network Science: Introduction to CINET 2015

5. The Open Graph Viz Platform
TOOLS Gephi
The Open Graph Viz Platform
A standalone GUI application
An interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs
Static and dynamic networks
Clustering and hierarchical graphs, community detection
Visualization layouts supported: ForceAtlas, Yifan's Hu Multilevel
Modular architecture customizable with plugins
Runs on Windows, Linux and Mac OS X
Implemented in Java. Graph size <1M nodes & edges
Open-source and free of charge
Network Science: Introduction to CINET 2015

6. Graph Visualization Software
TOOLS Graphviz
Graph Visualization Software
A graph description language (called DOT) and a set of tools that can generate and/or process DOT files
Can be used as standalone tool or as a library
Only graph drawing
A wide range of layouts:
Hierarchical or layered drawings
Spring model layouts
Multiscale layout for large graphs
Radial layouts
Circular layouts
Implemented in C
Runs on Linux, Windows and Mac OS X
Extensible through a scripting API
Open-source and free of charge
Network Science: Introduction to CINET 2015

7. Network Science: Introduction to CINET 2015
TOOLS Pajek
Pajek and Pajek-XXL
A standalone GUI application
Several partitioning and community detection algorithms
Network generator (random, Bernoulli/Poisson, scale free, small world, etc.)
Support for ordinary (directed, undirected, mixed) as well as multi-relational networks, bipartite, and temporal networks
Capable of analyzing and visualizing large networks with thousands or even millions of nodes
Macro capability enables recording and playback of a sequence of primitive commands
Implemented in Delphi (Pascal). Only Windows OS are supported (32 and 64 bit)
Freely available for noncommercial use
Network Science: Introduction to CINET 2015

8. Stanford Network Analysis Platform (SNAP)
TOOLS SNAP
Stanford Network Analysis Platform (SNAP)
A general purpose network analysis and graph mining library
Written in C++ but Python interface is also available
Scales to massive networks with hundreds of millions of nodes, and billions of edges
Efficiently manipulates large graphs, calculates structural properties, generates regular and random
graphs, and supports attributes on nodes and edges
Also available through the NodeXL which is a graphical front-end that integrates network analysis into Microsoft Office and Excel
Network Science: Introduction to CINET 2015

9. Network Science: Introduction to CINET 2015
What is CINET
A web-based tool for analyzing networks that represent interactions in large-scale complex systems
A large set of networks and algorithms to analyze networks
Ability to add user networks and have them analyzed by the algorithms available in CINET
The web-based interface has been designed to simplify the analysis of complex networks for users who are not necessarily computer scientists
Network Science: Introduction to CINET 2015

10. Creating an account with GRANITE
CINET Registration
Creating an account with GRANITE
Go to the login page
Click “Register” to create an account
Fill in the “Request Account” form and click “Register Account”
Use your username and password to log into the system
Network Science: Introduction to CINET 2015

11. Network Science: Introduction to CINET 2015
CINET Structural organization
Client-server model
Network Science: Introduction to CINET 2015

12. Network Science: Introduction to CINET 2015
CINET Architecture
Layered architecture
Network Science: Introduction to CINET 2015

13. Network Science: Introduction to CINET 2015
CINET Apps
Tools in CINET
Structural Analysis Tool (Granite)
190+ networks (graphs)
20+ network generators
70+ network algorithms (measures): GaLib, SNAP (Stanford), NetworkX
Visualization of networks: Gephi
Service for adding new networks (graphs)
Service for adding new structural analysis tools (graph algorithms)
Graph Dynamical System Calculator (GDSC)
Complete network dynamics on networks
Analyzing the phase structure of GDS; small graphs
13 graph templates; 15 vertex function (behavior) families
Simulation of Dynamics (EDISON)
Forward trajectory (dynamics) on networks
Compute (contagion) dynamics on larger networks: simulation
Services to manipulate attributed networks and to run simulations
Several contagion models: with and without interventions
Network Science: Introduction to CINET 2015

14. Computational engines and resources
CINET Components
Computational engines and resources
GaLib: provides efficient implementations of various classical and new graph algorithms that are motivated by the analysis of social contact graphs and disease dynamics on such graphs.
NetworkX: a powerful Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Stanford Network Analysis Platform (SNAP): a general purpose network analysis and graph mining library.
Both traditional high performance computing clusters, e.g., Shadowfax, Pecos (Virginia Tech), and cloud computing infrastructure, e.g., FutureGrid. An intelligent resource manager chooses appropriate computing platform for a network analysis job considering resource availability and computational and memory requirement.
Network Science: Introduction to CINET 2015

15. Network Science: Introduction to CINET 2015
CINET Features
Available features
Network Analysis
Network Generators
Network List
Measure List
Visualization
NetScript
Dynamic Analysis (in upcoming versions)
Network Science: Introduction to CINET 2015

16. Network Science: Introduction to CINET 2015
CINET Datasets
Networks
Social, web/internet, biological, infrastructure and transportation, artificial, and other types of networks
Currently 194 public datasets are available:
Amazon product co-purchasing
American College Football
DBLP Collaboration
Enron
Gowalla friendship
Wikipedia Who-votes-on-whom …
Public networks are available to any CINET user
Users can also upload their own datasets and make them public or private
Two different representations of the networks are supported:
Adjacency list (Galib) format
Edge list (NetworkX) format
Network Science: Introduction to CINET 2015

17. Network Science: Introduction to CINET 2015
CINET Analysis Tools
Network analysis
Graph Algorithms
Over 70 algorithms with variety of types related to shortest path, sub graph and motif counting, centrality, graph traversal, etc.
Dynamic Analysis
Multiple different simulation codes to provide different diffusion models and simulation capabilities. Analysis of the phasic structure of a graph dynamical system (e.g., spreading dynamic phenomena such as rumors through networks).
Network Generators
Implementation of ~20 random and deterministic network generators such as Barabási–Albert, Erdős–Rényi, small world, star graphs, etc.
Network Science: Introduction to CINET 2015

18. Network Visualization
CINET Visualization
Network Visualization
​An integrated visualization module that supports dynamic range of visualizations. Multiple layout algorithms: Random, Force Atlas, Yifan Hu, etc.
Feature based organization: determining node size and color by degree, betweenness, etc.
Coloring communities: applying community detection algorithm to visualize different communities in different colors.
Vector graphics output (SVG).
Network Science: Introduction to CINET 2015

19. Using CINET in education and research
CINET Applications
Using CINET in education and research
Network science courses
Virginia Tech, Blacksburg, VA
North Carolina A&T State University, Greensboro, NC
Jackson State University, Jackson, MS
University at Albany – State University of New York, Albany, NY
Research
We the People (WtP) project: Web-enabled petitioning system
Other petitioning sites (change.org)
Case studies
Network Science: Introduction to CINET 2015

20. Network Science: Introduction to CINET 2015
CINET Summary
CINET in Context
User interface—all user interaction – No need to program – No need for HPC resources.
Types of analysis – Network structural characteristics – Dynamics on networks
Large networks – Generation – Analysis
Multiple tools provided under a CINET umbrella
Crowd-sourced platform – Self-sustaining – Self-managing
Collaborative science
Community resource
Network Science: Introduction to CINET 2015

21. Papers and other publications
CINET References
Papers and other publications
Abdelhamid S, Alo R, Arifuzzaman S, Beckman P, Bhuiyan M, Bisset K, Fox E, Fox G, Hall K, Hasan S, Joshi A, Khan M, Kuhlman C, Lee S, Leidig J, Makkapati H, Marathe M, Mortveit H, Qiu J, Ravi S, Shams Z, Sirisaengtaksin O, Subbiah R, Swarup S, Trebon N, Vullikanti A, Zhao Z (2012) CINET: A CyberInfrastructure for Network Science. In The 8th IEEE International Conference on eScience, Chicago, IL, October 8-12, 2012.
Abdelhamid S, Alam M, Alo R, Arifuzzaman S, Beckman P, Bhattacharjee T, Bhuiyan H, Bisset K, Eubank S, Esterline A, Fox E, Fox G, Hasan S, Hayatnagarkar H, Khan M, Kuhlman C, Marathe M, Meghanathan N, Mortveit H, Qiu J, Ravi S, Shams Z, Sirisaengtaksin O, Swarup S, Vullikanti A, Wu T (2014) CINET 2.0: A CyberInfrastructure for Network Science. In The 10th IEEE International Conference on eScience,
Abdelhamid et. al., “GDSCalc:  A Web-Based Application for Evaluating Discrete Graph Dynamical Systems,” PLOS One 2015. …
Network Science: Introduction to CINET 2015

22. Network Science: Introduction to CINET 2015
CINET Links
Useful links
Main CINET page
Granite page
Stanford Network Analysis Project
Network Science: Introduction to CINET 2015

23. Network Science: Introduction to CINET 2015
CINET Hands-on Labs
Overview
Exercise 1
Learn how to use CINET through the Granite interface
Compute simple network measures
Exercise 2
Analyze a larger set of networks with CINET
Use the output of CINET to compute additional network measures and study correlations between graph parameters
Exercise 3
Use CINET to visualize networks
Explore different layouts and visualization parameters
Network Science: Introduction to CINET 2015

24. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Objectives
In this exercise
Review networks and measures available in CINET
Practice setting up network analysis and using different measures
Compute three measures for each of the two networks (Dolphins Social Network in New Zealand and Erdős Collaboration Network). Fill in the following table:
Network
# of nodes
# of edges
Density
# of triangles
Diameter
Dolphins 62
159
Erdős
6,927
11,850
Network Science: Introduction to CINET 2015

25. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Procedure
Follow these steps
Set up a new analysis (click on the “+New Analysis” button and choose a name for the analysis).
In the search box under the “Networks” heading , type “Dolphins” (without the quote marks).
The name of the network (“Dolphins Social Network in NZ”) appears below the search box. Select the network by clicking on the check box. If necessary, additional networks can also be selected for analysis.
Click the “Continue” button above “Networks”; the system then displays the menu for “Add measure”.
In the search box under “Measures”, type “Density” (without the quote marks).
The Density measure appears below the search box. Select the measure by clicking on the check box. It is possible to compute multiple measures as part of the same analysis. Use measures called “Compute the Number of Triangles” and “Find Diameter of a Graph” provided by CINET.
Click the “Analyze” button above “Measures”.
The system starts the computation and displays the “Status” of the computation.
When the “Status” appears as “COMPLETED”, click on the “View Report” link.
In the resulting window, click on the log.out link to see the answer and record the answer in the table above.
Network Science: Introduction to CINET 2015

26. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Outcome
Exercise review
What kind of networks are publicly available in CINET?
What network analysis measures does CINET offer?
Analysis results for the networks:
Network
# of nodes
# of edges
Density
# of triangles
Diameter
Dolphins 62
159 ≈ 95 8
Erdős
6,927
11,850 ≈
5,973 4
Network Science: Introduction to CINET 2015

27. Average node degree (∆)
CINET Hands-on Labs Exercise Objectives
In this exercise
Compute three measures for each of the five networks. Fill in the following table:
Determine whether certain pairs of graph measures are correlated using Pearson Correlation Coefficient (PCC) as the measure of correlation. Draw scatter plots.
Network
# of nodes
# of edges
Average node degree (∆)
# of triangles (T)
Diameter (D)
Autonomous systems - Oregon
10,729
21,999
Δ 1 =
𝑇 1 =
𝐷 1 =
Erdős Collaboration Network
6,927
11,850
Δ 2 =
𝑇 2 =
𝐷 2 =
Autonomous systems - Oregon
10,670
22,002
Δ 3 =
𝑇 3 =
𝐷 3 =
Autonomous systems - Oregon
10,900
31,180
Δ 4 =
𝑇 4 =
𝐷 4 =
Enron Giant Component
33,696
180,811
Δ 5 =
𝑇 5 =
𝐷 5 =
Network Science: Introduction to CINET 2015

28. Pearson Correlation Coefficient
CINET Hands-on Labs Exercise Pearson Correlation Coefficient
Pearson Correlation Coefficient
Suppose we are given a data sample consisting of n ≥ 1 pairs of numbers 𝑥 1 , 𝑦 1 , 𝑥 2 , 𝑦 2 , …, 𝑥 𝑛 , 𝑦 𝑛 . Let 𝑥 and 𝑦 denote respectively the mean values of the sets 𝑋= 𝑥 1 , 𝑥 2 , …, 𝑥 𝑛 and 𝑌= 𝑦 1 , 𝑦 2 , …, 𝑦 𝑛 ; that is 𝑥 = 𝑖=1 𝑛 𝑥 𝑖 𝑛 , and 𝑦 = 𝑖=1 𝑛 𝑦 𝑖 𝑛 . The Pearson Correlation Coefficient (PCC) r for the sample is given by 𝑟= 𝑖=1 𝑛 𝑥 𝑖 − 𝑥 𝑦 𝑖 − 𝑦 𝑖=1 𝑛 𝑥 𝑖 − 𝑥 2 𝑖=1 𝑛 𝑦 𝑖 − 𝑦 2 where positive square roots are used for both terms in the denominator. The PCC value r defined above satisfies the condition −1 ≤ r ≤ 1. The value r = 1 indicates that a linear equation describes the relationship between the two sets X and Y . Similarly, r = −1 indicates a linear relationship between the two sets, with Y values decreasing as the X values increase. The value r = 0 indicates that X and Y are not correlated.
Network Science: Introduction to CINET 2015

29. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Procedure
Follow these steps
Set up a new analysis in CINET and select the appropriate networks.
For each of the five networks, find the average node degree (use a measure called “Degree Statistics” provided by CINET), the number of triangles, and the diameter. Since each of the five networks is connected, all five diameter values should be finite.
Compute the PCC value 𝑟 1 for the sample Δ 1 , 𝑇 1 , Δ 2 , 𝑇 2 , …, Δ 5 , 𝑇 5 using a tool of your choice (a calculator, an Excel spreadsheet, by writing a simple program, etc.).
Compute the PCC value 𝑟 2 for the sample Δ 1 , 𝐷 1 , Δ 2 , 𝐷 2 , …, Δ 5 , 𝐷 5 using a tool of your choice.
Prepare two scatter plots, one showing the pairs Δ 1 , 𝑇 1 , Δ 2 , 𝑇 2 , …, Δ 5 , 𝑇 5 and the other showing the pairs Δ 1 , 𝐷 1 , Δ 2 , 𝐷 2 , …, Δ 5 , 𝐷 In each case, please show the ∆ values along the X axis and the other value along the Y axis.
Network Science: Introduction to CINET 2015

30. Average node degree (∆)
CINET Hands-on Labs Exercise Outcome
Exercise review
Is there a correlation between the network measures you computed? If so, what kind of correlation it is and why? What does it tell you about the networks?
Analysis results for two networks:
Network
# of nodes
# of edges
Average node degree (∆)
# of triangles (T)
Diameter (D)
Autonomous systems - Oregon
10,729
21,999
Δ 1 =4.101
𝑇 1 =15,834
𝐷 1 =12
Erdős Collaboration Network
6,927
11,850
Δ 2 =3.421
𝑇 2 =5,973
𝐷 2 =4
Autonomous systems - Oregon
10,670
22,002
Δ 3 =4.124
𝑇 3 =17,144
𝐷 3 =10
Autonomous systems - Oregon
10,900
31,180
Δ 4 =5.721
𝑇 4 =82,856
𝐷 4 =9
Enron Giant Component
33,696
180,811
Δ 5 =10.732
𝑇 5 =725,311
𝐷 5 =13
Network Science: Introduction to CINET 2015

31. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Outcome
Exercise review
The PCC values are 𝑟 1 ≈ and 𝑟 2 ≈
Scatter plots
Network Science: Introduction to CINET 2015

32. CINET Hands-on Labs Exercise 3 Objectives
In this exercise
Review layout algorithms and visualization parameters available in CINET
Create visualizations for the following networks:
Network
# of nodes
# of edges
Karate 34 78
American College Football
115
613
Amazon product co-purchasing
262,111
617,438
Network Science: Introduction to CINET 2015

33. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Procedure
Follow these steps
Switch to the “Networks” tab.
In the search box under the “Networks” heading , type “Karate” (without the quote marks).
The name of the network (“Karate network”) appears below the search box. Click the network to select it.
Set up a new visualization (click on the “+Add Visualization” button and choose a name for the visualization).
Select “Random” as the Layout Algorithm.
Click the “Generate” button at the bottom of the screen to produce the visualization. The system starts creating the visualization and displays the “Viz request submitted” status.
Click the “Visualization” link to switch to the visualization pane. If the system is still displaying the “QUEUED”, “RUNNING”, or “DOWNLOADING RESULTS” prompt wait until rendering is done. Check the status by clicking on the visualization name to refresh the pane.
Click on the network visualization to view it in a vector format (SVG). Save the SVG file on your local filesystem.
Create additional visualizations for the same network with the following parameters:
Once you have multiple visualizations you can switch between them by clicking on visualization names in the pane header.
Follow the same procedure to create visualizations for other networks.
Layout
Node
Node size
Node Min Size
Node Max Size
Random
Degree
None 1 10
Force Atlas
Modularity 5
Network Science: Introduction to CINET 2015

34. Exercise review What layout algorithms does CINET offer?
CINET Hands-on Labs Exercise Outcome
Exercise review
What layout algorithms does CINET offer?
What are some possible ways of reducing clutter when visualizing large networks
Network visualizations
Karate, Force Atlas layout, modularity node parameter, node size: degree, node min size: 5, node max size: 10
American Football, Force Atlas layout, modularity node parameter, node size: degree, node min size: 5, node max size: 10
Karate, random layout, degree node parameter
Network Science: Introduction to CINET 2015

35. Network Science: Introduction to CINET 2015
CINET Hands-on Labs Exercise Outcome
Exercise review
Network visualizations
Amazon product co-purchasing, Force Atlas layout, modularity node parameter, node size: degree, node min size: 5, node max size: 10
Network Science: Introduction to CINET 2015