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!