Mining Networks through Visual Analytics

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Mining Networks through Visual Analytics

• Incremental Hypothesis Building and Validation

David Auber Romain Bourqui Guy Melançon
CNRS LaBRI UMR 5800 INRIA Futurs
GRAVITÉ Bordeaux, France
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peacokmaps.com
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InfoVis CyberInfraStructure Pajek

• A picture is worth a thousand words
• Chinese proverb (?)

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Tulip BubbleTree
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Graph Viz Framework Tulip

• Its all visual
• R. Feynman (Nobel prize in Physics)

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Internet traffic
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Voronoï Treemaps

• The purpose of computing is insight not numbers
  
• R. Hamming (1973)

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Cushion Treemaps
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• Visualization uses
  computer
  graphics to help provide insight on complicated
  problems, models or systems
• Scientific visualization is exploring data and
  information graphically, gaining understanding
  and insights into the data
• R.A. Earnshaw (a pioneer in computer graphics,
  1973)

Munzners Hyperbolic Browser
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Tulip Sugiyama Layout
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Visualize?

• Inselberg creator of parallel coordinates
•  Insight through images 
•  Goal Visual Model to Help our Intuition 
•  Involves Geometry, Cognition, Art ? 

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Visualize?
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Visual graph mining related to security issues

• Recognize structural properties
• Identify key actors
• Identify their neighborhood
• Community structure
• Connectivity between communities

Chess players recognize patterns
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Example from NCTC data

• Extracted about 8000 incidents from WITS
• Identified terrorists groups when possible
  (directly or through AFP)
• Identified countries where incidents took place
• Added territorial information (continents, world
  regions) to help organize the overall map

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Example from NCTC data

• About 8000 incidents
• 9419 nodes
• 18486 edges
• Layout is time consuming
• Does not provide clue about structure
• Filter out incidents with no identified group

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Example from NCTC data

• Interactivity
• Play with network
• Apply various metrics
• Attribute-based node filtering
• Tulip Graph Viz Framework
• Opensource
• Plug-in architecture
• www.tulip-software.org

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Massive data

• Information big bang - Projet  How much
  information , Berkeley University
• In 2001, about 1 exabyte (1 million terabytes) of
  data is generated annually worldwide, including
  99.997 available only in digital form
• In 2003 each individual produces about 800
  megabytes per year

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Massive data

• 100 million FedEx transactions / day
• 150 million VISA transactions / day
• 300 millions long distance calls / day over ATTs
  network
• 35 billions e-mails / day over the world
• 600 billions IP packets / day over DE-CIX backbone

Keim, VIEW Workshop 2006
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Visualization and Moores law
Daniel Keim - Keynote Address, VIEW 2006
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Visualization and Moores law

• Issues that wont be solved by hardware only
• Design interaction together with visualization
• Understand how and why visualization pays
• Collaborate with other fields
• Integrate visualization together with other
  technology

NIH-NSF Visualization Research Challenges Report,
2006
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Added value of visual and interactive mining

• KDD Panel   The Perfect Data Mining Tool 
  Ankerst 2002
• The human eye is an excellent tool for spotting
  natural patterns
• Getting rid of the human in the loop? Wrong
  decision!
• Increase human participation through
  visualization in the data exploration and
  knowledge discovery processes

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Sense making loop
J. Thomas Visual Analytics Initiative
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Visualization mantras

• Visual Information Seeking Mantra
• Overview, Zoom-in / Filter, and Details on Demand
  (Shneiderman, 1996)
• Visual Analytics Mantra
• Analyse first, Show the Important, Zoom, filter
  and analyse, Details on demand (Keim 2006)

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Visualization pipeline

• A designers view on the visualization process

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Visualize?
Protein interaction network (yeast) Barabàsi 2000
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Organize data prior to visualization

• Layer or hierarchize data based on
• node/edge metrics (eigenvalues, centralities, )
• topological feature detection
• Use relevant drawing methods
• Combine with interaction

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Case study ITA 2000 passenger air traffic

• Cities connect through direct flights
• Edge weights number of passengers
• Questions
• Read motivations of carriers through organization
  of the network?
• Territorial logic?
• Political? Economical?

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Case study ITA 2000 passenger air traffic

• Cities connect through direct flights
• Edge weights number of passengers
• Questions
• Read motivations of carriers through organization
  of the network?
• Territorial logic?
• Political? Economical?

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TopoLayout (Topological) Feature-based
Hierarchization

• Search the graph for components of growing
  complexity
• Subtrees
• Biconnected components ( blocks )
• Grid-like
• Clusters

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TopoLayout (Topological) Feature-based
Hierarchization

• Search the graph for components of growing
  complexity
• Subtrees
• Biconnected components ( blocks )
• Grid-like
• Clusters

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TopoLayout (Topological) Feature-based
Hierarchization

• Search the graph for components of growing
  complexity
• Subtrees
• Biconnected components
• Grid-like
• Clusters

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TopoLayout (Topological) Feature-based
Hierarchization

• Search the graph for components of growing
  complexity
• Subtrees
• Biconnected components
• Grid-like
• Clusters

• Need to identify articulation points (pivots)
• The graph builds into a tree of biconnected
  components

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TopoLayout (Topological) Feature-based
Hierarchization

• Search the graph for components of growing
  complexity
• Subtrees
• Biconnected components ( blocks )
• Grid-like (eigenvalues)
• Clusters

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TopoLayout (Topological) Feature-based
Hierarchization

• Search the graph for components of growing
  complexity
• Subtrees
• Biconnected components ( blocks )
• Grid-like (eigenvalues)
• Clusters

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TopoLayout

• Components naturally organize as a hierarchy
  through the search process

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TopoLayout interaction Grouse

• Explore the graph by unfolding/folding the
  hierarchy
• The users navigation triggers layout of
  components
• Higher level graphs (quotient graphs) are built
  from metanodes
• Improve readability / Less visual elements
• Faster layout, based on topology of quotient
  graph
• Grouse

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TopoLayout interaction Grouse

• Multilevel hierarchy recursive grouping of
  metanodes

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TopoLayout interaction Grouse

• Multilevel hierarchy recursive grouping of
  metanodes

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TopoLayout interaction Grouse

• Multilevel Hierarchy for Abstraction Cut

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Multilevel navigation of small world networks

• Small world networks social networks, web
  graphs, transportation networks (ITA),
• Small world networks organize into several levels
  (hierarchy) Adamic, Huberman
• Idea capture the hierarchy and use it as a
  navigation paradigm

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Small world networks

• Centralities
• Bottleneck passageways
• Network organizes around those pivots nodes

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Small world networks

• Centralities
• Betweenness centrality has high computational
  cost (global)
• Betweenness centrality
• Eigenvalue centrality
• Prefer local index
• Degree
• Edge strength

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Small world networks

• Edge strength proportion of cycles containing an
  edge (length 3 and 4)

(Jaccard 1912) (Tanimoto 1958) Auber et al.
2003 Raddichi et al. 2004
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Small world networks

• Edge strength
• Costs linear time if degree is bounded, otherwise
  quadratic

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Small world networks

• Edge strength
• Cost yet lower than most centralities (local
  versus global indices)
• Incremental local modification of graphs require
  local recomputation

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Community structure of small world networks

• Filter out weak edges
• Capture components
• Infer quotient graph (metanodes)
• Recurse over each component

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Community structure of small world networks

• Filter out weak edges
• Capture components
• Infer quotient graph (metanodes)
• Recurse over each component

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Community structure of small world networks

• Filter out weak edges
• Q. What threshold to choose?
• A. Best possible one (!)
• Use quality criteria
• MQ (modularity quality)

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Quality criteria MQ

• C (C1, C2, , Cp) is a clustering of a graph G

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MQ / Nice properties

• MQ varies over a bounded interval -1, 1
• MQ behaves like a Gaussian distribution

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MQ / Nice properties

• MQ behaves like a Gaussian distribution

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Challenge find the best possible clustering
(according to MQ)

• Exhaustive search intractable
• Optimization, search algorithms (hill climbing,
  genetic algorithms, bio-mimetics, ) costy
• Heuristic exploit node/edge centralities
• Filter out weak edges
• Tickmark possible values for edges
• Find threshold with best MQ

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Filter / Threshold
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Filter / Threshold
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Filter / Threshold
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Hierarchical organization of the network

• The procedure can be iterated to produce a
  hierarchy of clusters
• Strength of edges is recomputed at each stage
• Threshold is locally chosen for each component

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MQ / Extension

• To take into account the relative size of
  clusters
• (MQ also naturally extends to fuzzy clustering)

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MQ / Extension

• Extend to various classes of graphs (where F
  stands for any adequate edge density function)

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Conclusion Future work

• MQ / Extension to graph hierarchies

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MQ / Extension to graph hierarchies

• Inspired from attribute grammars

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Conclusion Future work

• Study dynamic network
• Streamed / Time-stamped network
• Incremental/local computation/adjustment of
• edge metrics (local metrics)
• MQ (or other possible quality criteria)

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Conclusion

• Interaction is the real added value of
  visualization
• Must combine with other mining techniques
• Insert combination in sense making loop

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Conclusion

• We are opened and interested to collaborate with
  colleagues from other areas, adopting different
  perspectives
• Learning / Mining /
• Experts / Corporate organizations / Final users
• Any idea for a different multilevel clustering
  criteria/approach?

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Conclusion

• We are opened and interested to collaborate with
  colleagues from other areas, with other
  perspectives
• Learning / Mining /
• Experts / Corporate organizations / Final users
• Go visit Tulips website and download the
  software (Im here until Friday if you need a
  coach !)
• www.tulip-software.org
• Guy.Melancon_at_labri.fr

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Credits

• LaBRI UMR 5800, Bordeaux -- Equipe GRAVITÉ /
  INRIA Futurs
• Guy Melançon
• Maylis Delest
• David Auber
• Patrick Mary
• Tulip Graph Viz Framework
• www.tulip-software.org

• R. Bourqui, U Bx, FR
• D. Archambault, UBC, CA
• T. Munzner, UBC, CA

_at_labri.fr