visualization of anomalies in dynamic networks with nodexl
TRANSCRIPT
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Large Dynamic Networks and Patterns Visualization in NodeXL
Jacopo CirroneGraduate Student at University of Catania(Faculty of Computer Science Engineering)
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Networks of different genres in the Real World
Social Transportation Biological, Chemical
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Why Visualization is important?
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Improving our understanding of networks
Networks sources
Network.txt
Network.dbNetwork.xml
Networks graphs
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Improving our understanding of networks
Vizster [Heer 2006]
Infovis Co-authoring Network [Börner et al. 2004]
Clustering
Discovering the structure of the network
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Visualization of Networks that evolve over time
Whitfield et al, J of. MBC 2002
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Overview
• Introduction
• Large temporal networks Visualization in NodeXL
• Significant Anomalies Visualization in NodeXL
• Demonstration
• Conclusion and plan
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ObamaCare Twitter Network
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New Importer for Dynamic Network
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Time
Dynamic Networks Visualization
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Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Conclusion and plan
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Significant Anomalous Patterns Visualization
o Important Definition:o Pattern: Connected region of the graph that spans a certain
time interval with score higher than a given thresholdo For instance:
o Highway Network: low average speed on congested regions
Traffic Reported Accidents
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Others Anomalous Patterns Examples
o Biology: Most essential pathways in a cell cyclephase? Activation patterns?
o Smart Grid: Energy consumption patterns for better planning of generation, storage and transportation.
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Load Anomalous Patterns (SigSpot)
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Reported Accidents
PATTERNS
Black = Overlapthose edges or nodes belonging to two or more different patterns in the given time interval Grey = No Patterns
Pattern
Pattern
Pattern
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Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan
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Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan
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Behind the Visualization
o Let’s suppose we have:o All the Info about the Dynamic Network and the
Patterns in a text filePROBLEM:
HOW TO LOAD THOSE INFO IN ORDER TO GET AND VISUALIZE THEM IN A VERY FAST WAY?
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Behind the visualization – Solution A
HOW TO LOAD THE Network And Patterns INFOS IN ORDER TO GET AND VISUALIZE THEM IN A VERY FAST WAY?
PROBLEM:
This Solution is not efficient for large networks
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Behind the Visualization – Solution B
Network.db or
Patterns.db
Berkeley Database
HOW TO LOAD THE Network And Patterns INFO IN ORDER TO GET AND VISUALIZE THEM IN A VERY FAST WAY?
PROBLEM:
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Berkeley Database
QUERY
Refresh Worksheet
Refresh Graph
Behind the Visualization – Solution B
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Network-TREE BERKELEY DATABASE
[2,2][1,1] [4,4][3,3] [6,6][5,5] [8,8][7,7]
[1,8]
[1,4] [5,8]
[1,2] [3,4] [5,6] [7,8]
Array Sum [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Array Max [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Array Min [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]Array Avg [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Node or Edge AggregateNR NL
Generic NODE CONTENT
QUERY
6
1
2
3
4
5
AGGREGATE [4,6]
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Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan
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Conclusion
o This extension can be very useful for future researchers who are interested on:o Visualization of time evolving networkso Visualization of patterns within such networks
o We successfully managed networks witho Several thousands of nodeso Several thousands of edgeso Tens of thousands of time slices
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Plan
o Extend the application to allow the user to import a network with different formats
o Extend the functionalities of patterns visualization to make the application more user-friendly:o User should detect immediately the edges or
nodes belonging to a certain patterno User should detect immediately the time interval
where a certain pattern is defined
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Thanks!o Collaborators:
o Prof. Alfredo Ferro at Dept of Computer Science at Catania University
o Misael Mongiovi, Research Scientist at Dept of Computer Science UC Santa Barbara
o Prof. Ambuj K. Singh at Dept of Computer Science at UC Santa Barbara
Questions?