key-node-separated graph clustering and visualization takayuki...
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Key-node-separated graph clustering and visualization
Takayuki ItohOchanomizu University, Japan
China-Japan Joint Visualization Workshop2017/7/24
Itoh Laboratory, Ochanomizu University
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Career of the speaker
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Itoh Laboratory, Ochanomizu University
• 1992 Researcher of IBM Tokyo Research Lab.– 1992 M.S. at Waseda Univ.– 1997 Ph. D. at Waseda Univ.– 2000 Visiting researcher at Carnegie Mellon Univ. (6 month)– 2003 (Concurrent) researcher at Kyoto Univ. (2 year)CAD / SciVis / InfoVis / Distributed Computing
• 2005 Professor at Ochanomizu Univ.– 2005 Associate professor– 2008 Visiting researcher at Univ. California Davis (2 month)– 2011 Full professorInfoVis / Multimedia / HCI / CG applications
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Organizing …
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Itoh Laboratory, Ochanomizu University
• International– 2014, 2018 IEEE PacificVis Organizing/General chair– 2015 VINCI General Chair– 2012-2015 ACM SAC Multimedia&Visualization Track Chair– 2016 ACM Advanced Visual Interface Associate Program Chair– 2018 ACM Intelligent User Interface Student Volunteer Chair
• Japanese Domestic– 2014-2016 Director of Society for Art and Science– 2015-2017 Chief Program Committee of Interaction Symposium– 2018-2019 Director of SIG on Interactive System & Software
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Women-viewpoint projects
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Skin measurement and synthesis
Music user interface
Cartoon icon generation
Crowdsourcing of photo retouch
Apparel product recommendation
Crowdsourcing ofwomen’s appearance evaluation
Itoh Laboratory, Ochanomizu University
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Still implementing by myself
• Hierarchical data visualization
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Itoh Laboratory, Ochanomizu University
Itoh et al., Hierarchical Data Visualization Using a Fast Rectangle-Packing Algorithm, TVCG 2004
Itoh et al., Hierarchical Visualization of Network Intrusion Detection Data in the IP Address Space, CG&A 2006
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Still implementing by myself
• Network data visualization
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Itoh Laboratory, Ochanomizu University
Itoh et al., Key-node-Separated Graph Clustering and Layout for Human Relationship Graph Visualization, CG&A 2015
Itoh et al., A Hybrid Space-Filling and Force-Directed Layout Method for Visualizing Multiple-Category Graphs, PacificVis 2009
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Still implementing by myself
• High-dimensional data visualization
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Itoh Laboratory, Ochanomizu University
Itoh et al., High-dimensional data visualization by interactive construction of low-dimensional parallel coordinate, JVLC 2017
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Itoh Laboratory, Ochanomizu University
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Itoh Laboratory, Ochanomizu University
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Graph Visualization
• Computer-powered graph drawing
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Itoh Laboratory, Ochanomizu University
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Graph drawing … Long history
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Itoh Laboratory, Ochanomizu University
Euler’s drawing in 1736 Ball’s abstract drawing in 1892
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Graph drawing … Hard work
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Itoh Laboratory, Ochanomizu University
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Computer saved graph drawing
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Itoh Laboratory, Ochanomizu University
• Automatic• Quick• Interactive• Publishable
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Visual representation
• Node-link diagram • Matrix representation
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Itoh Laboratory, Ochanomizu University
A
E
D C
B
A B C D E
A
B
C
D
E
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Data types
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Itoh Laboratory, Ochanomizu University
Undirected Directed
Unfixed
Fixed
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Applications: Social analysis
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Itoh Laboratory, Ochanomizu University
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Applications: Bioinformatics
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Itoh Laboratory, Ochanomizu University
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Applications: Traffic/Communication
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Itoh Laboratory, Ochanomizu University
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Hairball problem
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Itoh Laboratory, Ochanomizu University
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Node layout (force-directed)
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Itoh Laboratory, Ochanomizu University
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Node clustering as a preprocess
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Itoh Laboratory, Ochanomizu University
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Edge bundling as a postprocess
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Itoh Laboratory, Ochanomizu University
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Interaction for graph visualization
• Immersive environment
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Itoh Laboratory, Ochanomizu University
• Zooming interfaceFocus+Context
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Complex data
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Itoh Laboratory, Ochanomizu University
Time-varying or interactiveaddition / removal of
nodes or edges
AssociatedTime-varying / multivariate
values at nodes / edges
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Coordinate view for complex data
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Itoh Laboratory, Ochanomizu University
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Graph visualization: summary
• Computer-powered graph drawing• Techniques
– Node layout– Node clustering– Edge bundling– Interactions
• Data types– Direction, Node positions
• Applications– Social analysis, Bioinformatics, Traffic, Communication, …
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Itoh Laboratory, Ochanomizu University
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Itoh Laboratory, Ochanomizu University
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Contents
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T. Itoh, C. Muelder, K.-L. Ma, J. Sese, A Hybrid Space-Fillingand Force-Directed Layout Method for Visualizing Multiple-Category Graphs, IEEE Pacific Visualization Symposium,pp. 121-128, 2009.
Itoh Laboratory, Ochanomizu University
The third place of the papers presented at IEEE PacificVis on the number of citations!
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Definition: Multiple-Category Graph
• Graphs consisting of nodesbelonging to one or more categories
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Itoh Laboratory, Ochanomizu University
},{ LNG },...,{ 1 nNnnN
},...,{ 1 nLllL
},...,{ 1 mi bbn
GraphNodesLinksA NodeA Link },{ qpi nnl
Category belonging information(array of boolean values)
Drawing example (color=category)Drawing example (color=category)
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Easy example: Social Networking
• Node = Person• Link = Friendship• Category = Community/Keyword
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Itoh Laboratory, Ochanomizu University
CakeCake ViolinViolinSoccerSoccer
Tight sub-networkTight sub-network
Multi-community personMulti-community person
Hub personHub person
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Requirements for Visualization
• Place common-category nodes closer• Reduce:
– sum of lengths of edges– number of intersections among edges
• Avoid the cluttering of nodes• Maximize screen space utilization• Reduce the computation time
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Itoh Laboratory, Ochanomizu University
Multiple-category graph specific
General
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Requirements Satisfaction
• Place common-category nodes closer• Reduce:
– sum of lengths of edges– number of intersections among edges
• Avoid the cluttering of nodes• Maximize screen space utilization• Reduce the computation time
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Itoh Laboratory, Ochanomizu University
Force-directed
Space-filling
Hybrid Space-Filling and Force-Directed Method
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Hybrid Approach: Overview
Step1: Hierarchical clustering– Categorized & Non-categorized– Category & Connection based
Step 2: Layout– Rectangle packing for Non-categorized nodes– Hybrid for categorized nodes
Step 3: Interaction– Focus+Context– Category selection
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Itoh Laboratory, Ochanomizu University
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Step 1: Hierarchical Clustering
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(1) Root
(2) Top of categorized nodes
(3) Clusters of categorized nodes
(4) Clusters of non-categorized nodes
(5) Categorized nodes (6) Non-categorized nodes
Itoh Laboratory, Ochanomizu University
Category & connection based clustering
Connection based clustering
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Step 2: Layout
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Itoh Laboratory, Ochanomizu University
Root
Top of categorized nodes
w
Clusters of categorized nodes
Clusters of non-categorized nodes
Categorized Non-categorized
Data structure Layout
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Rectangle Packing Technique
• Originally for tree visualization [Itoh04][Itoh06]
• Treemap-like, but better on:– Aspect ratio– Flexible cluster positioning (by referring templates)
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Itoh Laboratory, Ochanomizu University
Input Result
Tree
Template(Optional)
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Switching Layout Algorithms
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Itoh Laboratory, Ochanomizu University
Root
Top of categorized nodes
w
Clusters of categorized nodes
Clusters of non-categorized nodes
Categorized Non-categorized
Data structure
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Switching Layout Algorithms
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Itoh Laboratory, Ochanomizu University
Root
Top of categorized nodes
w
Clusters of categorized nodes
Clusters of non-categorized nodes
Simply apply rectangle packing
Consider both connection & category
Rectangle packing is not always good
New approach(Hybrid rectangle packing
& force directed)
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Hybrid Layout for Categorized Nodes
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Itoh Laboratory, Ochanomizu University
(b) Force-directed layoutfor cluster graph
(c) Positions as a template (d) Rectangle packing
(a) Clustering ofcategorized nodes
Edge weight is proportional to the
number of linksConnect if their
category is common, even if there is no links
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Drawing Nodes
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Colors of nodes denote categories
Categorized nodes are drawn as
colored circles
There are 3 levels of thickness and
transparency of links
Non-categorized nodes are drawn
as gray dots
Radii of nodes denote number of links
Three colors denote that the node belongs
to three categories
Itoh Laboratory, Ochanomizu University
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Result (1) Zooming categorized nodes
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Itoh Laboratory, Ochanomizu University
No cluster-cluttering
A
BC
A,B,C: Good concentrationof common-category nodes
D
D: Good for discoveryof isolated categories
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Comparison
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Itoh Laboratory, Ochanomizu University
Space-Filling (1)Space-Filling (1) Space-Filling (2)Space-Filling (2) ProposedProposed
1.3 (sec.) 1.2 (sec.) 4.7 (sec.)
* Force-directed … 267.5 (sec.)
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Comparison
• Three criteria– Node distance, edge length, & num. intersection
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0
0.5
1
1.5
2
2.5
3
3.5
Node distance Edge length Num. intersect.
S.-F. (1)S.-F. (2)Proposed
Itoh Laboratory, Ochanomizu University
* Relative values (1 for proposed technique)
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Focus+Context
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Itoh Laboratory, Ochanomizu University
Non-categorizednode concentration
Space distortionfor focus+context
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Applied Active Biological Data
• Data– 6,152 genes (as nodes)– 7,564 gene-gene interactions (as edges)– 10 conditions of gene expression (as categories)
• Goal– Hub / Multi-functional gene discovery– Sub-network discovery by conditions
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Itoh Laboratory, Ochanomizu University
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Visualization Example
[A] Hub gene discovery
[B] Multi-functional gene discovery
[C] Well-divided sub-network discovery
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Itoh Laboratory, Ochanomizu University
A
A
B
C
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Visualization Example
[D,E,F] Separation of common-condition gene clusters
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Itoh Laboratory, Ochanomizu University
D
D'E E'
F
F'
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Itoh Laboratory, Ochanomizu University
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Contents
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Itoh Laboratory, Ochanomizu University
IEEE Computer Graphics & Application, 2015Organized by Xiaoru, Baoquan, Koji, & Issei
T. Itoh, K. Klein, Key-node-Separated Graph Clustering and Layout for Human Relationship Graph Visualization,IEEE Computer Graphics and Applications, Vol. 35, No. 6,pp. 30-40, 2015.
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Human Relationship Graph
• Node=Human, Edge=Relationship– Paper co-authorship– Friendship on SNS
• Characteristics– Authority persons– Topic-based clusters
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Human Relationship Graph
• Node=Human, Edge=Relationship– Paper co-authorship– Friendship on SNS
• Characteristics– Authority persons– Topic-based clusters
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Issues on node clustering
• Community finding schemes are well applied– Extracting subgraphs which have dense edges
• Issue: “key nodes” are involved in large clusters• Often we want to separate such nodes from clusters
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(a) Clustering based ondensity of connections
(1) (2)
(b) High-level drawingof the clustered graph (a)
Key node and many edgesare hidden inside clustersKey node and many edgesare hidden inside clusters
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Issues on node clustering
• Community finding schemes are well applied– Extracting subgraphs which have dense edges
• Issue: “key nodes” are involved in large clusters• Often we want to separate such nodes from clusters
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(3)
(4)
(5)
(c) Clustering based oncommonality of neighbors
(d) High-level drawingof the clustered graph (c)
• Key nodes are more visible• Many edges are bundled• Key nodes are more visible• Many edges are bundled
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Visualization with key-node-aware clustering
• Two metrics for node-to-node distances– (Dis-) Commonality of connected nodes– (Dis-) Similarity of feature vectors
• Used for:– Node clustering– Node layout
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Similar feature vectors=Persons with similar topics
Commonly connected nodes=Authority persons
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Data Structure & Node Distance
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Graph={Node, Edge}Node={n1, n2, …}Edge={e1, e2, …}
A node hasa feature vectorni={ai, ….}
An edge connectstwo nodes ei={ni1, ni2}
Data structure Node distance
∝ 1.0 ∝
Itoh Laboratory, Ochanomizu University
· /: feature vector of a node
→Similarity of topics
1.0/ 1: number of common adjacent nodes
→Connection to same persons
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Clustering & Layout
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Hierarchical clustering → Cluster Layout → Node Layout• Centroid method • MDS & Stress minimization
• Laplacian smoothing• Swapping in a circle
Itoh Laboratory, Ochanomizu University
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Edge Bundling
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(a) Edge bundling between two clusters of nodes
Node Node
Center ofcluster
Center ofcluster
Node Node
Center ofcluster
Center ofcluster
(b) Placement of control points of Bezier curves
Itoh Laboratory, Ochanomizu University
Before
After
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Example Dataset
• Paper co-authorship data– 564 papers by NBAF (NERC Biomolecular Analysis Facilities)– 1,821 nodes(=authors), 11,097 edges(=co-authorships)
• Feature vectors from paper titles– Frequency of 12 words for each author → 12 dim. Vector
• Computation time– 2.5 sec. for clustering– 8.8 sec. for node layout
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Genetic (Red), Molecular (Orange), Loci (Yellow),Microsatellites (Yellow green), Isolation (Green),Inbreeding (Blue green), Transcriptomics (Sky blue),Expression (Blue), Bacterial (Indigo),Breeding (Purple), Polymorphic (Pink)
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Node Layout Example
• Clusters as circles → Nodes inside the circles• Colors according to feature vectors• Edge display control
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Clusters of nodes
Feature-based colors
Edges of a node
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Key-node Separation from Large Clusters
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170 clusters, by our algorithm
* Color=degree of a node
Two key nodes are separatedfrom large clusters
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Key-node Separation from Large Clusters
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159 clusters, by common algorithm
Itoh Laboratory, Ochanomizu University
* Color=degree of a node
Two key nodes are involvedin a large cluster
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Case Study with Co-authorship Graph
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Expression
Genetic
Isolation
Polymorphic
Molecular
* Colors are based on feature vectors
Genetic (Red), Molecular (Orange), Loci (Yellow),Microsatellites (Yellow green), Isolation (Green),Inbreeding (Blue green), Transcriptomics (Sky blue),Expression (Blue), Bacterial (Indigo),Breeding (Purple), Polymorphic (Pink)
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Case Study with Co-authorship GraphItoh Laboratory, Ochanomizu University
* Colors are based on degree of nodes
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Case Study with Co-authorship GraphItoh Laboratory, Ochanomizu University
* Colors are based on degree of nodesCluster A
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Case Study with Co-authorship Graph
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One of the key persons in cluster A:Many connections with particular fields of people
Expression
Genetic
Molecular
Polymorphic
Breeding
Transcriptomics
Loci
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Case Study with Co-authorship Graph
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Another key person in cluster A:More variety of connections with many fields of people
Expression
Genetic
Molecular
Polymorphic
Breeding
Transcriptomics
Loci
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Case Study with Co-authorship GraphItoh Laboratory, Ochanomizu University
* Colors are based on degree of nodes
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Case Study with Co-authorship GraphItoh Laboratory, Ochanomizu University
* Colors are based on degree of nodes
Cluster B
Cluster C
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Case Study with Co-authorship Graph
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A key person in cluster B:Many connections with other fields of people
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Case Study with Co-authorship Graph
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A key person in cluster C:Many connections with uncolored fields of people
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Numeric comparison
Num. clusters Num. nodes of clustersof two key nodes
Num. edgesinside clusters
Ours (1) 813 4, 4 5964Ours (2) 354 4, 4 5421Ours (3) 264 4, 4 5868Ours (4) 170 9, 9 6141Common 159 33, 54 8214
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Successfully separatedkey nodes from large clusters
Smaller number of edgesare hidden inside clusters
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Subjective Evaluation
Data 1(ours)
Data 1(common)
Data 2(ours)
Data 2(common)
Q: Interested in key nodes?
9 4 10 3
Q: Find the clusters connected to key nodes?
9 4 7 6
Q: Find the number of nodes connected to key nodes?
10 3 8 5
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Participants: 13 university students in computer science
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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On-going work: for directed graphs
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[Toeda16]
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Three types of bundling for directed graphs
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A. Edges betweentwo clusters of nodes
B. Edges in the pair of bidirectionalbundles connecting to thesame pair of clusters
C. Edges of pairs of bundles whichstart or end at the same cluster
Itoh Laboratory, Ochanomizu University
[Toeda16]
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Potential Applications
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Itoh Laboratory, Ochanomizu University
Paper Citation Network Music Ordering
Graph Drawing
Time-Varying
Visual Analytics
GPU-based
Tree Evaluation
Pops
Rock
Slow Ballad
Blues Dance
Jazz
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Contents
• Graph Visualization Overview• Multiple-Category Graph Visualization• Key-Node-Separated Graph Visualization
– Concept & Algorithm– Experiment– On-going work
• General Discussion
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Current research situation
• Slowly growing research field– Constant number of papers every year– But still remaining many essential problems– Recent meetings of theoretical and application people
(e.g. NII Shonan meeting in 2015/01 and 2016/08)• Mostly used by professional people
– Social analysis, bioinformatics, system monitoring, …– Less popular applications for general users
• General software vs. Application-oriented tool
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My interested directions
• Numeric evaluation of comprehensibility– Are mathematically beautiful visualization results
really good?• Automatic algorithm selection
– Especially node clustering and layout algorithms• User interfaces
– Newer devices (touch panels, VR/AR, …)– Voice processing, Natural language processing
• New application development
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