SeeGraph: A System for Visualizing Weighted-Edge Graphs - PowerPoint PPT Presentation

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SeeGraph: A System for Visualizing Weighted-Edge Graphs

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SeeGraph: A System for Visualizing WeightedEdge Graphs – PowerPoint PPT presentation

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Title: SeeGraph: A System for Visualizing Weighted-Edge Graphs


1
SeeGraphA System for Visualizing Weighted-Edge
Graphs
  • Joshua New
  • Dr. Jian Huang
  • Dr. Elissa Chesler

2
Overview
  • System Diagram
  • Algorithmic Tools
  • Screenshot

3
System Diagram
4
System Diagram
  • .wel Weighted Edge List correlations with
    format
  • NumVerts lttabgt NumEdges ltnewlinegt
  • Vertex1 lttabgt Vertex2 lttabgt WeightV1toV2
    ltnewlinegt...
  • .gvl Graph Vertex Layout - contains a 3D
    position (as a short int -32k,32k) in format
  • Comment line ltnewlinegt NumVerts lttabgt NumEdges
    ltnewlinegt
  • Vertex1 lttabgt V1_X lttabgt V1_Y lttabgt V1_Z
    ltnewlinegt
  • .btv Block Tridiagonalization Vector BTDs
    permutation vector with NumVerts tab-delimited
    integers of row/col indices

5
System Diagram
  • .txt Database Info - features file with
    format
  • NumRows lttabgt NumCols ltnewlinegt
  • Row1Col1 lttabgt Row1Col2 lttabgt... Row1ColN
    ltnewlinegt
  • .map Database Index maps vertex names in the
    .txt file to data in .wel file with format
  • VertexI ltnewlinegt VertexJ ltnewlinegt...
  • _labels.map Vertex Names used to display on
    the graph when desired with format
  • VertexI_DisplayName ltnewlinegt
  • VertexJ_DisplayName ltnewlinegt ...

6
Algorithmic Tools
  • Graph Layout O(MV2)

7
Edge Coloring
H o t / C o l d
NS0-.5,.5-1
N!S0-1
!NS0-.15,.85-1
!N!S.85-1
F u l l C o l o r
8
Dynamic Threshold
P A R A M 2
0.85 - 0.91
0.92 - 0.95
0.85 - 0.91
P A R A M 3
0.92 - 0.98
9
Algorithmic Tools
  • Block tridiagonalization (BTD)
  • Eigenvalues of the final matrix differ from those
    of the original matrix within a specified error
    tolerance
  • The final matrix has small relative bandwidth
  • Any offdiagonal blocks in the final matrix have
    either low dimension or are close to a low-rank
    matrix

10
Algorithmic Tools
  • Adjacency Matrix and BTD Visualization

11
Algorithmic Tools
  • Database query (quantitative selection)
  • Neural Network (qualitative selection)

12
Algorithmic Tools
  • Context
  • Graph algorithms (induced subgraph, LCC, etc.)

13
Screenshot
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