Visualizing Massive Multi-Digraphs James Abello Jeffrey Korn Information Visualization Research Shannon Laboratories, AT&T Labs-Research All the graphs copied from “Visualizing massive Multi-Digraphs”
Dec 31, 2015
Visualizing Massive Multi-Digraphs
James AbelloJeffrey Korn
Information Visualization ResearchShannon Laboratories,AT&T Labs-Research
All the graphs copied from “Visualizing massive Multi-Digraphs”
Massive Graph Visualizer (MGV)
Visualization and exploration system for massive multi-digraph navigation.
Assumes a vertex set of the underlying digraph corresponds to leave sets.
Out-of-core graph hierarchy and visual representation of each hierarchy slice.
Implemented in C and Java 3D. Applied in geographic information
systems, telecommunications traffic and internet data …
Problems with data visualization
Massive data size Bottlenecks
– I/O bandwidth – Screen
SolutionHierarchical graph slices
Traditional graph representation
Traditional nodes and edges representation of a fully connected graph with 20 nodes
Hierarchical graph slice rationale(1)
Build hierarchical multi-digraph layers on top of input multi-digraph.
Each layer is obtained from coalescing disjoint sets of vertices at previous level
In short, convert multi-digraph data into hierarchical data structure.
V sets, E sets Root, Leaves, Height
Hierarchical graph slice rationale(2)
Layer of each level is a subgraph with vertex and edges , so called Hierarchical Graph Slices.On each slice, less nodes, much less edges.
Handling two bottlenecks
The original graph is in the external memory, tree is computed and stored in RAM. Engine needs to computes one slice for interface at a time upon request.
Panoramic 3D display provides hierarchical and horizontal navigation thru all nodes and edges.no information lost
Slice View Interfaces
MGV provides flexible interface. Works on adjacency representation
matrix.similar to representation of Needle Grid.
Handle massive data :AT&T call detail multi-digraph has 275million daily increment on 260 million vertices.
Needle grid
Edge maps into
a little tick Lines weighted By color, length, width, orientation
Star Maps
Rearrange matrix into circular
histogram Well focused Detail data
triggered By mouse
Multi-comb
stack of star maps,single
object represent aggregated view of
millions of edges. 3D coordinates facilitates
data evaluation. Useful for animation of data
evolution
Multi-wedge
Each wedge is the distribution spectrum of a state.
2D
Aggregated views
Simply splice the segment to single bar User move the cursor into the bar for part information
Usability metrics
• Ease of Use & Navigation• Good First Impression• High User Retention over
Time• High Learnability• Lesser number of user
errors
Conclusion on MGV Computational engine + Java based user
interface– Engine runs at a web server, communication thru
XML.– Java provides fast renderingHierarchical algorithm facilitates navigation
on slice, actually integrates visualization and computation.
Large class of massive data sets.
Questions ?and
thank you!