visualization of anomalies in dynamic networks with nodexl

Large Dynamic Networks and Patterns Visualization in NodeXL

Jacopo CirroneGraduate Student at University of Catania(Faculty of Computer Science Engineering)

Networks of different genres in the Real World

Social Transportation Biological, Chemical

Why Visualization is important?

Improving our understanding of networks

Networks sources

Network.txt

Network.dbNetwork.xml

Networks graphs

Improving our understanding of networks

Vizster [Heer 2006]

Infovis Co-authoring Network [Börner et al. 2004]

Clustering

Discovering the structure of the network

Visualization of Networks that evolve over time

Visualization of Networks that evolve over time

Whitfield et al, J of. MBC 2002

Overview

• Introduction

• Large temporal networks Visualization in NodeXL

• Significant Anomalies Visualization in NodeXL

• Demonstration

• Conclusion and plan

ObamaCare Twitter Network

New Importer for Dynamic Network

Time

Dynamic Networks Visualization

Overview

• Introduction

• Large temporal networks Visualization

• Significant Anomalies Visualization

• Demonstration

• Conclusion and plan

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

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.

Load Anomalous Patterns (SigSpot)

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

Overview

• Introduction

• Large temporal networks Visualization

• Significant Anomalies Visualization

• Demonstration

• Business logic Explanation

• Conclusion and plan

Overview

• Introduction

• Large temporal networks Visualization

• Significant Anomalies Visualization

• Demonstration

• Business logic Explanation

• Conclusion and plan

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?

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

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:

Berkeley Database

QUERY

Refresh Worksheet

Refresh Graph

Behind the Visualization – Solution B

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]

Overview

• Introduction

• Large temporal networks Visualization

• Significant Anomalies Visualization

• Demonstration

• Business logic Explanation

• Conclusion and plan

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

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

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?