VISUALISATION AND ANALYSIS OF RELATIONNAL DATA BY

CONSIDERING TEMPORAL DIMENSION

Eloïse Loubier and Bernard Dousset

Institut de Recherche en Informatique de Toulouse

118 route de Narbonne, 31062 Toulouse Cedex 9

Keywords: Graph drawing, relational analysis, actor network, semantic network, morphing, alliance, evolutionary

graph, space/time analogy.

Abstract: Visualization based on graph drawing allows the identification, the evaluation of passed and present

structures between actors and concepts. It also allows the deduction of future ones. VisuGraph is developed

in order to offer to the users the visualization and the interactive classification of relational data. We

propose to complete this prototype with a morphing algotihm which animates with fluidity the

representation between different time periods, emphasizing major elements and significant tendencies.

1 INTRODUCTION

Within strategic information framework, graphs

appear as a tool of synthetic and intuitive

representation of actor networks or semantic

networks. Remarkable topologies are thus identified,

revealing relationships between the various actors

(authors, laboratories, companies, country) and the

terms and/or concepts. Moreover, the study of the

evolution of a network structure in time

(collaborations, co-quotations, co-signatures, co-

occurences, alliances, fusions, acquisitions,

licences,…) allows the evaluation of their last and

current organizations. It makes it possible to deduce

from them their future organizations and their

implications in order to make a decision.

In this context, our research institute proposes

the Tetralogie platform for the visualisation of

relational data. Dedicated to macro analyses, it

makes it possible (remotely and with several users)

to carry out strategic analyses starting from

heterogeneous textual data, by the means of methods

of traditional or innovative data analysis. The

prototype VisuGraph adds to this platform

visualization and interactive classification of

relational data.

In this article, we will focus on analysing the

dynamicity of relational data networks considering

their evolution.

Firstly, we present works on the visualisation of

relational information in order to allow the analysis

of their evolution. Secondly, within the framework

of the platform Tetralogie, we propose new

functionalities for VisuGraph for the visualisation of

the evolution of the various networks and to analyze

the dynamics of their relations from a strategic point

of view,.

We implement a graph algorithm, which reveals

the successive structures by animating the graph

representation between various periods, significant

changes and determining actors and/or concepts. The

representation is based on spaces/time analogy used

for a clock. The objective is to obtain an intuitive

reading of the evolution by sequentially distributing

the periods on a dial. The representation is animated

in a similar way as how we play a video in a fast-

forward mode. The strategic placement of the nodes

allows then, not only, to locate them in time but also

to evaluate their persistence and to deduce the

evolution. We develop this approach while insisting

on data structures, the optimization of graph drawing

and its animation.

2 DATA VISUALISATION

The analysis of the evolution of relational

information is typically based on techniques of

dynamic graphs visualization.

Researchers have developed numerous network

visualisation systems, (DiBattista et al., 1999),

including internet connectivity maps, large networks

of telephone calls, the structure of research shown as

550

Loubier E. and Dousset B. (2007).

VISUALISATION AND ANALYSIS OF RELATIONNAL DATA BY CONSIDERING TEMPORAL DIMENSION.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - ISAS, pages 550-553

DOI: 10.5220/0002375305500553

 SciTePress

citation networks, and the progressive visualisation

of how a knowledge domain evolves.

Brandes (Brandes and Corman, 2003) presents a

system for the visualization of the networks

evolution in 3D. Figure 1 is carried out in the form

of layers representing the network for a given time

period. Each node corresponds to an entity remain,

placed in a particular position on each layer. Thus,

the visualization of the evolution is based only on

the links and not on the structure.

Figure 1: 3D representation of an evolutionary network:

Brandes (Brandes and Corman, 2003).

Erten (Erten et al., 2004) a visual analysis of the

evolution of the collaborations between researchers

of a given field. TGRIP produces a 2D

representations series (see figure 2), for each period,

linking all the nodes common to each period. Nodes

and edges of the graph have a weight calculated

according to the structure of the graph.

Thus, each node has a size in relation with its

weight. The weight of an edge is used to calculate

the attraction force between the nodes during the

graph rendering.

Figure 3, generated by CiteSpace Chen (Chen,

2004), visualizes the most salient co-citation

network of articles published in a domain subject. In

this figure, the graph is based on researches realised

in graph rendering domain. It patches individual

snapshots of co-citation networks taken from

different time slices into a panoramic view. The

important visual and structural attributes include

pivots points

A pivot is a joint between individual snapshots.

Represented on figure 3, it joins branchs surrounded

by a dark black cercle (1993-1995) and branchs

surrounded by a light black cercle (1999 – 2001).

Figure 2: 2D representation of the evolution of

collaborations with TGRIP: Erten et al. (Erten et al.,

2004).

Figure 3: A co-citation map of graph drawing articles

(1990 – 2003) by CiteSpace: Chen (Chen, 2004).

3 TETRALOGIE PLATFORM

3.1 Principles

In strategic information context, IRIT, our research

institute, proposes a powerful tool for the

visualisation of relational data: Tetralogie.

This platform makes it possible (remotely and with

several users) to carry out strategic analyses starting

from heterogeneous textual data. Tetralogie is

composed of two parts:

a handling system of corpus allowing to manage

files resulting from download or interrogations of

CD. By various tools, it allows to extract matrices of

crossing

by considering into account each base and each

format specificity.

- an analysis system of the information contained

in the matrices, which is articulated around a

specific 3D spreadsheet. and which uses innovating

data on static, biparting or evolutionaring fields.

VISUALISATION AND ANALYSIS OF RELATIONNAL DATA BY CONSIDERING TEMPORAL DIMENSION

551

The VisuGraph prototype, a Tetralogie module adds

visualization and interactive classification of

relational data, in a comprehensive way and by

provides to the user a maximum of synthetic

information.

3.2 Representation of Evolutionary

Data

The relational data used result from information

treatments under Tetralogie. Data are represented in

matrices forms by crossed entities over several

temporal homogeneous segments (or time periods).

Then our work consists in transforming these

data into a networks representation, where the nodes

represent entities and the links define the relations

between them. It is possible to define a graph for

each value of the temporal dimension.

This solution makes it possible to only analyze

separately the period time compared to each other

and never combined. Another approach consists in

building the “total” graph G

1-n

, like the combination

of the graphs G

, G

,…, Gn of the n periods. The

total graph is associated to a matrix resulting from

the addition of the matrices of all the periods. The

advantage of this representation is to dispose of a

total sight of each data for each time period of the

analysis: total positioning.

4 VISUGRAPH PROTOTYPE

According to Tufte (Tufte, 1983), “an excellent

graph provides to the reader the maximum number

of ideas in shortest lapse of time by using less ink

and smallest space”. Based on this principle and on

Karouach’s works (Karouach and Dousset, 2004) we

propose to extend VisuGraph functionalities.

Relations are represented using a graph whose nodes

are the objects and the edges are the links

comparable to springs.

4.1 Graph drawing

4.1.1 Force_Directed Placement

In order to place the nodes as well as possible, we

have decided to use force_directed placement

functions applied on the nodes. These functions

follow generally accepted aestetic criteria for

graph

rendering, including evenly distributed vertices,

minimized edges crossings, and uniform edge

lengths.

According to Eades (Eades, 1984), a graph is

comparable with a spring model while taking as a

starting point the physical laws of graph drawing.

This system generates forces between the nodes that

involves their displacement. Attraction forces are

calculated only for neighboring nodes and repulsive

force are calculated for all pairs of nodes.

The attraction force between the nodes can be

proportional to the force of the bond between them.

The attraction force between two nodes υi and υj is

given by:

ƒa (υ

, υ

) = β

× d

αa /K (1)

is a function of the edge weight (υ

, υ

) and of the

nodes weight υ

and υ

. The factor K is calculated

according to the surface of the representation and the

number of graph nodes. d

is the distance between

, υ

in the representation.

If the nodes υ

, υ

are not connected by an edge then

ƒa (υ

, υ

) = 0..

The repulsion force between two nodes υ

and υ

defined by:

ƒr (υ

, υ

) = -K² / d

αr × β

(2)

The variable αr (resp. αa) is a constant which is used

to define the attraction degree (resp. repulsion)

between two nodes.

Starting from an initial state of strong energy, we

release the system until the nodes are harmoniously

positioned without superimposing themselves. On

the level of each node, the associated metric value is

represented by one or several histogram bar. The

static analysis can be at the origin of serious errors

in the interpretation over a long period, especially if

the visualisation is about non cumulative

phenomena.

As a consequence, it appeared necessary to add to

VisuGraph dynamicity for faithful and rigorous

analyses.

4.1.2 Dynamic Case

n order to add to our prototype a dynamic aspect,

we designed a morphing graph algorithm, which

reveals the successive structures, by animating the

graph between various time periods. It reveals the

significant changes and it determines actors and/or

concepts. The graph morphing allows to detect,

include/understand and even to predict the

significant tendencies, through the visualization of

data evolution, while being based on the spaces/time

analogy. In our case, nonvisible temporal references

represent the various time periods. They are fixed in

a chronological order and in an equidistant way on

ICEIS 2007 - International Conference on Enterprise Information Systems

552

the circumference of the display window (like the

hours on a dial).

Graph is influenced by the attribution of new

bonds connecting each node to the temporal

reference marks, which are related to the time period

considered. It generates a displacement, locating

each node next to the marks of the time periods that

it belongs. After stabilization of the graph, each

peripheral sector of the window corresponds to a

typology of particular evolution, only the center can

contain several types of persistence (continuous

presence or over a few spaced periods).

The graphs of various periods can be represented

individually, by simply hiding nodes and bonds not

concerned by the selected period. It is then possible

to detect, for example, an emergent structure or an

organisational change and to check the relevance of

the following period level.

The representation of the nodes as evolution

histogram makes it possible to locate them in time;

For example, if the upper-left part (last reference)

contains a majority of recent nodes, it is here where

we must seek the famous weak signals and try to

envisage their evolution.

The distribution of the other nodes is carried out

randomly. In the dynamic approach, the drawing of

the same total graph makes it possible to locate the

nodes according to their specific periods.

5 CONCLUSION

VisuGraph appears as an ergonomic and powerful

tool for dynamic data analysis which makes it

possible to reveal, include/understand and anticipate

the subjacent structures in order to identify their

strategic implications. We have demonstrated the

potential of an integrative approach to the

visualization and analysis of a research field

evolution. In particular, we have focused on various

practical issues concerning detecting emerging

trends and abrupt changes in transient research

fronts. The encouraging results indicate that this is a

promising line of research with the potentially wide-

ranging benefits to users from different disciplines.

This prototype is on its first steps ans requires

some improvements. The nodes are strongly

attracted by the temporal references, changing their

first position which took care of the relations with

the neighboring nodes. Thus, we would find a

compromise for a more flexible animation of the

movement between two time periods, then an

adapted cinematic for each time period.

Moreover, this morphing is conditioned by the

user point of view. It can be, for example, directed

towards the detection of strong signals (important or

persistent) or weak signals (appearances,

disappearances, reorganizations of actors which can

be potentially interesting). Thus, we must locate the

problems of each one precisely and draw the graph

while taking the user interests into account.

REFERENCES

Brandes U., Corman S., 2003. Visual unrolling of network

evolution and the analysis of dynamic discourse.

InfoVis'02(2), N°1, pp. 40-50.

Chen C, 2004. Searching for intellectual turning

points:Progressive Knowledge Domain Vizua-lisation.

Proceedings of the National Academy of Sciences of

the United States of America, 101(suppl. 1), pp. 5303-

5316.

http://www.pnas.org/cgi/reprint/0307513100v1.pdf

Chen C., Kuljis J., 2003. The rising landscape: A visual

exploration of superstring revolutions in physics.

Journal of the American Society for Information

Science and Technology, 54(5), pp. 435-446.

DiBattista G., Eades P., Tamassia R., Tollis IG, 1999.

Graph drawing:Algorithms for the visualisation of

graphs. Upper Saddle River, NJ:Prentice Hall, 1999.

Eades P., 1984. A heuristic for Graph Drawing.

Congressus Numerantium, vol. 42, pp. 149-160.

Erten C., Harding P., Kobourov S., Wampler K., Yee G.,

2004. Exploring the computing literature using

temporal graph visualization. Conference on

Visualization and Data Analysis.

Karouach S., Dousset B., 2004. Analyse d'information

relationnelle par des graphes interactifs de grandes

tailles. EGC’04, Clermont Ferrand.

Tufte E., 1983. The visual display of quantitative

information. Graphic Press. Cheshire, pp. 198,

Connecticut.

VISUALISATION AND ANALYSIS OF RELATIONNAL DATA BY CONSIDERING TEMPORAL DIMENSION

553