This is the authors’ preprint version. Please, find final version here: http://dx.doi.org/10.1177/1473871611425872

Visualizing Explicit and Implicit Relationsof Complex Information Spaces

Marian Dork, Sheelagh Carpendale, Carey WilliamsonUniversity of Calgary, Canada

ABSTRACT

In this work, we describe how EdgeMaps provide a newmethod for integrating the visualization of explicit andimplicit data relations. Explicit relations are specificconnections between entities already present in a givendata set, while implicit relations are derived from mul-tidimensional data based on similarity measures. Manydata sets include both types of relations, which are of-ten difficult to represent together in information visu-alizations. Node-link diagrams typically focus on ex-plicit data connections, while not incorporating implicitsimilarities between entities. Multidimensional scalingconsiders similarities between items, however, explicitlinks between nodes are not displayed. In contrast,EdgeMaps visualize both explicit and implicit relationsby combining graph drawing and spatialization tech-niques. We have applied this technique to three casestudies (philosophers, painters, and musicians) and ex-plored how integrated visualizations of explicit and im-plicit relations reveal novel patterns and relationships.

Keywords: Information visualization, explicit and im-plicit relationships, graph drawing, dimensionality re-duction, aesthetics, design, WWW.

1. INTRODUCTION

An important goal of information visualization is toreveal different types of relationships within abstractdata. Through interaction, the viewer can be enabledto find and understand connections between bits ofinformation. Relations can be explicitly present in adata set as links that specifically connect informationitems or implicitly by inferring relations based on sim-ilarity of attributes. For both types of relationships—explicit and implicit—several visualization techniqueshave been proposed and refined over the recent years.Two of the most popular techniques for visualizing re-lations are node-link diagrams (NLD) and multidimen-sional scaling (MDS). On the one hand, NLD tech-niques are usually applied to explicit relations or con-nections that are visualized as edges between nodes rep-resenting, for example, online communities, computernetworks, or linked web pages. On the other hand,MDS is typically used for implicit relations betweendocuments or other types of multidimensional data.MDS spatializes attribute similarities between items by

placing similar items in close proximity to each otherand less similar items further apart.

While NLD and MDS techniques are widely studiedand increasingly deployed, they both have significantlimitations with regard to readability and interpretabil-ity of the resulting visualization. Layout algorithms forNLD visualizations are typically optimized for reducingedge crossings, with the side effect that node positionsare not utilized as a meaningful visual variable. Fur-thermore, as the number of edges increases, it becomeshard to distinguish directionality, if present, and iden-tify high-degree nodes. The resulting visualization ofan MDS algorithm, on the other hand, lacks a guid-ing structure to put elements into context with eachother. It is often difficult to understand the meaningof the positional proximity of elements. We argue thatthe limitations of both techniques could be attributedto the fact that they are constrained to either explicitor implicit relations, yet, many data sets feature bothtypes of relationships.

In this paper, we explore how both explicit and im-plicit relations can be visualized as EdgeMaps, inte-grated views that combine graph drawing and spa-tialization. EdgeMaps integrate NLD and MDS tech-niques utilizing both visual linkage and proximity forthe representation of complex—explicit and implicit—relations between items. The intent behind this ap-proach is to make effective use of visual variables thathave been underutilized in NLD and MDS techniques.

As case studies for this paper, we have chosen datasets of philosophers, painters, and musicians from theFreebase data community. While there are many bio-graphical records associated with these prominent per-sonalities of philosophy, art, and music, a particular in-teresting aspect is the existence of influence connectionsbetween people, which are a type of explicit relations.On the other hand, birthdates, interests, movements,and genres are attributes that indicate implicit rela-tions between philosophers, painters, and musicians.We chose these dimensions as they provide a com-pelling use case for the visualization of explicit andimplicit relationships and let us explore complex datarelationships. Visualizing influences between musiciansor philosophers as edges may indicate who had moreimpact, yet, it is not possible with these links alone to

1

Figure 1. Visualizing relations among musicians. The influence of The Beatles is visualized in the similarity map.

see the extent of the impact. By encoding meaning intoboth position and edges, it becomes possible, for exam-ple, to explore the influence of musicians across genresor of philosophers over time (see Figures 1 and 2).

The remainder of the paper is structured as follows.First, we provide an overview of prior work (Section 2),after which we explain our design goals (Section 3) andthe data sets we use as case studies (Section 4). Wethen introduce the visual representations provided withEdgeMaps (Section 5) and describe the web-based in-terface design (Section 6). Using the case studies, we il-lustrate new ways for exploring complex relations (Sec-tion 7). We then discuss the limitations and open ques-tions of this work (Section 8) and conclude the paper.

2. RELATED WORK

As visualizing relationships is at the heart of informa-tion visualization, our work builds upon many previouscontributions in the field, in particular with regard tothe use of visual variables, graph drawing methods, andcasual visualization.

While not part of his visual information-seekingmantra (“Overview first, zoom and filter, then details-on-demand”), Shneiderman notes the challenge of be-ing able to explore relationships between informationitems.1 He stresses the importance of interaction for re-lating data entries, however, equally if not more impor-tant are the appropriate visual representations of differ-

2

Figure 2. Visualizing influence relations between philosophers; Friedrich Nietzsche is selected in the timeline view.

ent types of relations. To think about representing rela-tionships visually it is worth considering the visual vari-ables that are at our disposal. In Semiology of Graph-ics, Bertin distinguishes between eight visual variables:size, value, texture, colour, orientation, shape, and thetwo dimensions for the position on the plane.2 MDSrenderings use planar position as the primary visualvariable, while NLDs typically rearrange position in or-der to minimize edge crossings. Stone makes the casethat colour can make visualizations more effective andbeautiful when used well.3 She shows how colour canbe used for labelling and quantifying data. It would beinteresting to explore the use of colour for conveyingsimilarity between items as a degree of association inBertin’s terms.

There has been extensive research on drawing andinteracting with NLDs,4 often aiming at reducing edgecrossings, which is one of several geometrical and graph-theoretical metrics for graph aesthetics.5 Recent addi-tions to this research include EdgeLens, a technique forinteractively exploring overlapping edges,6 and EdgeBundles, a method for combining edges with similarpaths.7 Another problem of large graphs is occlusion,especially when arrowheads of directed edges impair theperception of the actual nodes. A study of directedgraphs examined a range of visual cues for directionalityand their effect on determining direct and two-step con-nections.8 While these contributions can significantlyimprove the readability of large NLDs, we argue thatcontextual attributes of graph elements need to be moreacknowledged. This perspective is supported in earlierwork on computer network visualization, where edge

and node attributes (e. g., flow, capacity, utilization) ofregional and international Internet links were regardedto be more important than the network topology.9 Aspart of a social network visualization it was shown howthe visual representation of number of friends, gender,and community structure enriches the NLD and allowsfor interactive filtering.10

While conventional NLD techniques focus almost en-tirely on explicit relations, MDS can be seen as a com-plementary approach focussing on proximity as a vi-sual representation of implicit relations or similarity.MDS has been used for document visualizations withthe goal to visually convey “thematic patterns and re-lationships” of text collections.11 While the idea of spa-tializing document collections based on their similari-ties or differences is promising, the resulting galaxiesand themescapes still appear abstract and difficult tointerpret. An approach to making MDS more inter-active focused on steering the algorithm, but did notlook at using interactivity to make the MDS view moremeaningful and accessible.12

Besides linkage and similarity, another important re-lation is based on the temporal dimension and refer-ences between temporally structured items. Consider-ing that time is generally seen as a linear dimension, thechallenge is to visualize relations between items that aremapped onto a linear axis. Arc diagrams show cross-references along a linear axis by adding visual semi-circles to linear views of documents, music pieces, andDNA sequences.13 In work that explored the use of arcsto visualize email threads, it was shown how the combi-nation of arcs displayed above and below the main axis

3

improved readability.14

Several contributions to information visualizationhave looked at enriching and combining techniques forvisualizing different types of relations. For example,a visual document hierarchy was accompanied witharcs representing cross-references between different sec-tions.15 Furthermore, NLDs were made more read-able by assigning nodes into multiple regions and allow-ing for interactive edge filtering.16 A related approachplaces graph nodes on a multi-variate grid allowing fornew forms of exploration.17 Another technique pro-vides a three-dimensional juxtaposition of different vi-sualizations on panes linking corresponding nodes usingedges between the panes.18 All these techniques under-line that there is a need for integrating explicit andimplicit relations and enabling their interactive explo-ration, however, there is a tendency to emphasize oneover another.

A related area within information visualization fo-cuses on social, casual, and artistic aspects of visualiza-tion. For example, Many Eyes is an online communitythat allows web users to upload their data sets, cre-ate and customize visualizations, and discuss these withother community members.19 Many Eyes is one exam-ple among many web-based visualization projects thatmake visualizations more readily available within theweb browser, which has become a major trend in visual-ization research and practice.20 The development thatvisualization is becoming part of everyday activities,moving beyond professional, specialized domains is alsodescribed as casual information visualization, implyingnovel design challenges and audiences.21 One exampleof this development is the increasing use of visualizationas a medium for artistic expression,22 which also aimsat creating beauty and triggering curiosity besides sup-porting data analysis and sense-making. Against thebackdrop of casual information practices with growinginformation spaces, considerations of aesthetics and cu-riosity also enter the domain of information seeking.23

3. DESIGN GOALS

The motivation behind this work is the multitude ofdata sets that feature both explicit and implicit rela-tions and preliminary research on complementing thevisualization of one type of relation with aspects of theother. For this work, we understand explicit relationsas data relations that specifically connect data entitiesand are already present in the data set. As implicit re-lations we see data relations that are not defined in thedata set and need to be inferred based on similaritiesbetween data items.

Explicit linkage between items has been the mainstayof graph drawing research. On the other hand, thereare numerous implicit similarities based on various pa-rameters and dimensions that can be used for data spa-tialization. With this work we are exploring the spaceof integrating the visualization of both explicit and im-plicit data relations in order to reveal previously unseenpatterns. In particular, we aim at supporting people inviewing complex data sets and exploring relationshipsbetween information entries in a pleasing and engagingway. This translates into the following design goals:

• Integrate multiple relationships. Explicit andimplicit relations should be visualized as linkageand layout to mutually support each other.

• Show invisible data patterns. By integratingexplicit and implicit data connections, the visu-alization should provide novel, interesting shapesand relations that were not visible before.

• Support serendipitous exploration. The visu-alizations should allow viewers to find unexpectedinsights and easily follow their interests. The in-teractivity necessary should be readily accessiblewithout requiring training.

• Display additional information. The interfaceshould provide detail-on-demand operations allow-ing the viewer to learn more about particular dataentries and to go back to the data source.

• Provide aesthetic visuals. The colours, shapes,and transitions used by the visualization shouldsatisfy both utility and visual appeal, making theinteraction pleasant and evoking curiosity.

4. DATA SETS AND DIMENSIONS

To illustrate the notion of complex information spacesand explore their visual representation we choose per-sonalities from philosophy, visual art, and popular mu-sic as exemplary data sets. We collected the data fromthe Freebase website using their web-based API.24 Free-base offers structured information about many entities.In the case of philosophers, painters, and musicians, itprovides data about birthdates, influence connections,and a range of domain-specific keywords such as philo-sophical interests, artistic movements, and musical gen-res. Besides programmatic methods of accessing thedata, Freebase also publishes tabular views of individ-ual data entries on their web site (see Figure 3). Asthe group of musicians has only sparse influence con-nectivity on Freebase, we complement our data set byinfluence information from the AllMusic website.25

4

Figure 3. One example data set for visualizing implicit andexplicit relations is philosopher data from the Freebase com-munity. Here is the entry about the French philosopherSimone de Beauvoir: description, birthdate and profession(top), interests and influences (bottom).

We focus on philosophers, painters, and musicianswho influenced at least one other member of their cor-responding group. The selection is also based on theavailability of keywords corresponding to the selectedsimilarity criteria that are most meaningful for thegiven group of people. For philosophers, we includetheir philosophical interests (e. g., ethics, logic, nature)and professions (e. g., writer, physicist, teacher). Thekeywords of painters include art forms (e. g., painting,mural, fresco) and artistic movements (e. g., renais-sance, impressionism, surrealism). For musicians, weincluded their genres (e. g., jazz, punk, electronic).

Based on the requirements of keyword and influ-ence information the resulting data sets comprised 196philosophers, 226 painters, and 200 musicians. For eachperson we keep the name, birthdate (for musical groups,year of formation), description, an image, and the key-words. Furthermore, we store the directed influencelinks between people, which we consider as explicit re-lations, and the keywords, which form the basis for com-puting similarities among people as implicit relations.The dates can also be regarded as another type of im-plicit relation as it implicitly links people of similar timeperiods or epochs. For storing these records, we use aMySQL database easily accessible from the server-sidecomponent of our system, which was written in PHP.

Using these attributes, several interesting dimensionscan be inferred. For example, we can calculate the de-

gree of influence as a measure of significance using thesum of outgoing influence connections. In other words,the more people a particular person has inspired and in-fluenced, the more significant this philosopher, painter,or musician is. Based on birthdates, people can also begrouped into similar epochs. Likewise, using domain-dependent keywords philosophers, painters, and musi-cians can be grouped by their similarity. Combiningtime periods and keywords with the influence connec-tions, one can look at the impact of, for example, mu-sicians across epochs and interests. For example, con-sidering all the musicians that were influenced by TheBeatles, their impact extended over a wide range ofmusical genres (see Figure 1). As Friedrich Nietzschewas considered influential by many subsequent philoso-phers, his impact is one of great temporal extent (seeFigure 2). By juxtaposing and integrating both explicitand implicit relations it is possible to reveal these rela-tions, which are not easily accessible by just looking atdata tables or reference pages.

5. VISUALIZING EXPLICIT ANDIMPLICIT RELATIONS

EdgeMaps provide a visualization that represents bothexplicit and implicit relations by integrating spatial-ization and graph-drawing techniques. Explicit rela-tions are encoded as curved edges and implicit rela-tions as node position. Other visual variables are usedto double-encode these data relations and introduce ad-ditional information such as directionality and distinct-ness. In the following, we describe our design consider-ations and decisions concerning visual representation.

5.1 Implicit Relations as Layout

To represent the implicit relations, we designed twogeneral layouts: a similarity map and a timeline, asillustrated in Figure 4. While both layouts representpeople as nodes and influences as edges, as we will de-scribe in more detail later, the layouts differ in the waythe positions on the plane are utilized. The similar-ity map represents the topical nearness among peoplebased on their domain-specific keywords such as inter-ests, movements, and genres. The timeline uses birth-dates as an ordering criteria to arrange philosophers,painters, and musicians along a temporal axis. Whileneither of these layouts are new by themselves, theyprovide new context for graph visualizations.

Similarity map. The node positions for the simi-larity map were generated using an MDS algorithm26

on the basis of the people’s keywords. To do this, thekeyword data is transformed into a vector model that

5

Similarity Map Timeline

Figure 4. Layouts for visualizing explicit and implicit rela-tions based on similarity (left) and birthdates (right).

the MDS algorithm uses as a basis to map relative dis-tances in the high-dimensional vector space to distancein the two-dimensional plane. The result is a pair of x,ycoordinates for each philosopher, with each coordinatevalue between -1 and 1. Depending on the size of thewindow, these coordinates are then scaled to the actualdisplay resolution. However, the aspect ratio of theMDS output is not modified, since the proximity of theresulting plane configuration is generated on the basisof similarity. Stretching the layout would confound therepresentation of similarities, which are mapped to po-sitions on the plane using their Euclidian distances. Asthere may be people with identical keywords, the MDSalgorithm can return items with the same positions,posing an occlusion problem. Considering that MDSis an approximation after all, the similarity map placesoverlapping nodes slightly apart so that they are stillclose but not occluding each other. The positions inthe similarity map are used to generate unique coloursfor all circles as discussed in more detail later. Theresulting layout is shown in Figure 5.

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Figure 5. Similarity map of philosophers without selection.

Timeline. The timeline maps birth year to posi-tion on the plane. Initial trials with a linear scalingwere not satisfying, because of dense clusters of peoplein some time periods and very sparse or empty peri-ods during other times. We decided to neglect exacttemporal intervals and focussed on temporal sequencesof people instead. This still allows for relative tempo-ral comparisons of ‘earlier’ and ‘later’ people and, atthe same time, accommodate all people’s nodes alongthe time axis. The result of this ‘stringing’ of nodesalong the axis has the effect that certain periods takeup much more display space than others. A time legenddisplayed on top of the time view and a subtle grid rep-resenting decades and centuries indicate this temporalfolding. As the data for the philosophers was especiallysparse between about 300 B.C. and 1200, there is aclustering of grid lines in this particular time area. Theresulting timeline is shown in Figure 6.

5.2 Explicit Relations as Curved Edges

The layouts for topical and temporal similarities repre-sent only the implicit relations between data items. Inorder to represent explicit relations, i. e., influences be-tween philosophers, painters, and musicians, directededges are drawn between the nodes. However, if alledges were to be shown for all nodes at the same time,there would be far too many edges to be readable, letalone interpretable. Instead, by activating only onenode at a time, it is possible to read individual edgesand differentiate between two types of influence:

• Incoming influence. The person is inspired or af-fected by other people and builds upon their work.

• Outgoing influence. The person has inspired oraffected other people who built upon their work.

A recent study on representing directed edges sug-gests that simple visual cues better help detectingconnections between two nodes than curves or arrow-heads.8 However, our aim is to reveal the extent ofmany connections of one node with the all the remain-ing nodes and discern incoming and outgoing influences.Incoming influence can be seen as the basis of a person’swork, the outgoing influence is the impact they had onother people. To visually differentiate between theseedge types, we used curvature, directionality, shape,opacity, and colour (see Figure 7).

Curvature. Incoming edges are curved downwardand outgoing edges are curved upward. The idea isthat incoming influence stands for the foundation uponwhich a philosopher, painter, or musician builds their

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Figure 6. Timeline of philosophers without selection.

work. For the outgoing influence, the edge is curvedupward, as a visual depiction of reach beyond previ-ous work. Taken together, both types of edges form awave-like shape when nodes are arranged in a sequen-tial order. The extent of curvature of an edge dependson its distance between the connected nodes. This way,the wider the reach of an influence, the more salient theedge appears. To reduce the visual footprint of edgesin the similarity map, the curvature is slightly smallerthan in the timeline view.

A B C

Figure 7. By selecting one node at a time, we can distinguishbetween two types of edges: incoming, B is influenced by A(left), and outgoing, B influences C (right).

Directionality. Incoming and outgoing edges differalso with regard to how directionality is represented. Aswe assume that only one person is activated at a time, itis most likely that this person’s node will have multipleincoming and outgoing edges, while all other connectednodes will mostly have only one associated edge or insome cases two edges (if the influence is mutual). Thisindicates that to most clearly represent edge direction-ality, there is much less clutter around the linked nodesrather than around the activated node. We exploit thisfact, by situating the visual representation of direction-ality at the linked, non-active nodes: incoming edgeshave arrow-like cuttings at their source nodes, whereasthe outgoing edges coming from the active node havearrows at the destination nodes. This ensures that theedge endings at active person’s node are simple andthin allowing for many discernible edges.

Shape. An additional way to differentiate the edgetypes is by their shapes. Incoming edges are drawnthicker than the outgoing edges. While there is no in-herent reason why incoming edges should be thickerthan outgoing edges, giving the different types of edgesthese two distinct shapes allows for easy distinctionwhen multiple edges are displayed at the same time.

Opacity. To balance out the different visual weightsresulting from thick and thin edges, the opacity of theincoming edges is decreased. Using opacity instead ofadjusting brightness for the incoming edges also reducesthe occlusion of other elements due to incoming edges.

Colour. Since the node have different colours, thecolour of an edge corresponds with the colour of thenode, where the influence is coming from. The outgo-ing influence edges take on the colour of the selectedperson, and the incoming edges share the colour withthe originating node. The mapping of nodes’ positionto their colour is discussed in the following section.

5.3 Redundancy through Size and Colour

EdgeMaps visualize data sets primarily using positionto represent implicit relations (time and similarity) andedges to represent explicit relations (influence). As sec-ondary visual variables we include node size and colourto accentuate significance and similarity of date items.

We understand significance of a person as the rela-tive degree of their outgoing influence. The rationale isthat a philosopher, painter, or musician who had moreinfluence on their peers and successors is arguable moresignificant than those that had less impact. Outgoinginfluence can be viewed by selecting the correspondingnode and viewing the outgoing edges. We decided toadditionally expose the significance of a node withoutany interaction, by encoding significance of a personas their circle’s area. We experimented with differenttypes of glyphs, however, we decided that simple circleswith varying sizes would allow for sufficient data encod-ing without adding significant perceptual complexity.

Furthermore, we decided to use colour to double-encode similarity based on people’s keywords. Sincethe output of the MDS algorithm provides a spatial-ization of this relationship, we used it in combinationwith the HSV (hue, saturation, value) colour space asthe basis for the colour calculation (see Figure 8). Wedecided to map broad interest regions to hue and thedistance to the centre to saturation, while keeping thebrightness constant. The idea behind this encoding isthat the further out a person is located, the more dis-tinct they are from all other people. Translating this

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Figure 8. Colours are derived from MDS positions usingdistance from centre for saturation and angle for hue.

notion of distinctness to a colour space, it would seemintuitive that the more distinct items would be moresaturated and the more common items would be lesssaturated. However, keeping the value constant meansthat either the nodes in the centre would be too brightor the nodes on the periphery too dark. Therefore, wetook the distance to centre into account for the bright-ness value resulting in well-visible, grey-like nodes inthe centre and colourful nodes towards the outside.

Based on this approach, nodes receive distinctcolours, which are also used for their outgoing influ-ence edges. As shown, for example, in Figure 9, bot-tom, the active philosopher’s outgoing edges have thecolour of this node and the incoming edges have thecolours of their respective philosophers. This also helpsto distinguish edge types and associate edges with cor-responding source nodes. Furthermore, as colours andsizes are consistently used between layouts the viewermay better recognize previously visited nodes.

5.4 Yarn Balls vs. Fireworks and Waves

After having discussed the visual representation of im-plicit and explicit relations individually, we will nowdiscuss how layout, edges, and additional encodingscome together in novel formations.

While exploring several design options, we examinedthe possibility of activating multiple nodes at the sametime. The result is an example of the “yarn ball” ef-fect for complex NLDs, conveying neither overview norstructure (see Figure 9, top). In contrast, displaying

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Karl Heinrich Marx (May 5, 1818–March 14, 1883) was a

German philosopher, political economist, historian,

sociologist, humanist, political theorist, and revolutionary

credited as the founder of communism.

Marx summarized his approach to history and politics in the

opening line of the first chapter of The Communist Manifesto

(1848): “The history of all hitherto existing society is

the history of class struggles.” Marx argued that

capitalism, like previous socioeconomic systems, will

produce...

Immanuel Kant (IPA: [ɪ'manuɛl kant]; 22 April 1724 –

12 February 1804) was an 18th-century German philosopher

from the Prussian city of Königsberg (now Kaliningrad,

Russia). He is regarded as one of the most influential

thinkers of modern Europe and of the late Enlightenment.

Kant created a new widespread perspective in philosophy

which influenced philosophy through the 21st Century. He

also published important works of epistemology, as well as

works relevant to religion, law, and history....

Friedrich Wilhelm Nietzsche (October 15, 1844 – August

25, 1900) (German pronunciation: [ˈfʁiːdʁɪç

ˈvɪlhәlm ˈniːtʃә]) was a nineteenth-century German

philosopher and classical philologist. He wrote critical

texts on religion, morality, contemporary culture,

philosophy, and science, using a distinctive German language

style and displaying a fondness for metaphor and aphorism.

Nietzsche's influence remains substantial within and beyond

philosophy, notably in existentialism and postmodernism....

René Descartes (French pronunciation: [ʁәne dekaʁt]),

(31 March 1596 – 11 February 1650), also known as Renatus

Cartesius (Latinized form), was a French philosopher,

mathematician, scientist, and writer who spent most of his

adult life in the Dutch Republic. He has been dubbed the

"Father of Modern Philosophy," and much of subsequent

Western philosophy is a response to his writings, which

continue to be studied closely to this day. In particular,

his Meditations on First Philosophy continues...

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Aristotle (Greek: Ἀριστοτέλης, Aristotélēs)

(384 BC – 322 BC) was a Greek philosopher, a student of

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subjects, including physics, metaphysics, poetry, theater,

music, logic, rhetoric, politics, government, ethics,

biology and zoology.

Together with Plato and Socrates (Plato's teacher),

Aristotle is one of the most important founding figures in

Western philosophy. He was the first to create a

comprehensive system of Western philosophy,...➜➜ TIME LINE

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INFLUENCE A B C

B influenced A, and B was influenced by C

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Immanuel Kant (IPA: [ɪ'manuɛl kant]; 22 April 1724 –

12 February 1804) was an 18th-century German philosopher

from the Prussian city of Königsberg (now Kaliningrad,

Russia). He is regarded as one of the most influential

thinkers of modern Europe and of the late Enlightenment.

Kant created a new widespread perspective in philosophy

which influenced philosophy through the 21st Century. He

also published important works of epistemology, as well as

works relevant to religion, law, and history....

➜➜ TIME LINE

LEGEND

SIGNIFICANCE

degree of influence1

47

INFLUENCE A B C

B influenced A, and B was influenced by C

BADIOU

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Jacques-Marie-Émile Lacan (French pronounced [ʒaklakɑ]) (April 13, 1901 – September 9, 1981) was a

French psychoanalyst and psychiatrist who made prominentcontributions to psychoanalysis, philosophy, and literarytheory. He gave yearly seminars, in Paris, from 1953 until1981, most influencing France's intellectuals in the 1960sand the 1970s, especially the post-structuralistphilosophers. His interdisciplinary work is Freudian,featuring the unconscious, the castration complex, theego;...

Figure 9. Displaying influence edges in the similarity map forfive philosophers (top) and one philosopher at a time (mid-dle and bottom). With a single activated philosopher, influ-ence edges arguably resemble the shape of fireworks. Lessinfluential philosophers have sparser edge patterns (middle)than more influential ones (bottom).

only the edges associated with one individual node al-lows for interesting visualization and interaction pos-sibilities (see Figure 9, middle and bottom). As dis-cussed before, in this way it becomes possible to bet-ter represent edge types and directions. Furthermore,considering that the node size reflects the number of

8

outgoing edges, differing node sizes may be a more ef-fective way to convey the general overview of a data set.Instead of displaying many edges that make readabil-ity more and more difficult with increasing number ofnodes and edges, it appears to be more effective to usecolour, position, and size for nodes to provide contextand overview.

An interesting structure emerges when selecting anode in the similarity map with incoming and outgoingedges rendered in patterns of fireworks (see Figure 9,middle and bottom). Relatively less influential peo-ple (e.g., Lacan) result in more modest fireworks thanthe most influential people, such as Kant. Besides thenumber of edges, the spatial extent of edges conveysthe topical scope of a person’s incoming and outgoinginfluences. In the temporal layout, the influence edgeslead to distinct wave-like patterns (see Figure 10). Theresulting edge layout is particularly interesting as thewaveform can also be seen as an engaging representa-tion of the dynamic evolution of philosophical ideas,artistic movements, and musical genres.

In the similarity map, the spatial extent of edgesstands for topical scope and in the timeline the ex-tent of edges represents temporal scope of influence. Itbecomes possible to distinguish influences within closetemporal or topical proximity from influences that spanmultiple time periods and similarity regions. There is aqualitative difference in influencing people of the sametime with similar characteristics than influencing peo-ple distributed over a larger time interval across a rangeof interests, movements, or genres. For example, theinfluence between two jazz singers is different from theinfluence that a blues musician has on a punk musician.In EdgeMaps these differences are revealed by the posi-tion, distribution, and length of edges, and the coloursand positions of associated nodes.

6. CREATING A WEB-BASEDVISUALIZATION INTERFACE

In addition to the representation of multiple types ofdata relations, it is important to consider how the inter-action and visual design of the interface can be realizedto support the exploration of complex relationships. Inthe following, we describe how we designed a web-basedinterface for EdgeMaps aimed to support establishedinformation visualization principles and integrate thetool into the web-browsing experience.∗

∗Demo online: http://mariandoerk.de/edgemaps/demo

6.1 Interacting with the Visualization

The interface provides a range of interaction techniquesfor manipulating the view, data, selections, and details.The primary way of interacting with the EdgeMaps vi-sualization is selecting a node representing a person andrevealing the corresponding influence connections com-ing towards this node and going out to other nodes. Theviewer can then change the selection by either selectinganother node (possibly one that is associated with thecurrent one) or by deselecting this node by clicking onthe background or clicking again on this node.

A text search allows the viewer to search throughnames, descriptions, and keywords. When a searchterm is entered, the visualization shows only the labelsof the nodes representing data items that are matchingthe search query; the opacity of the remaining nodes isdimmed and their labels are not shown. This way itis possibly to quickly see where, for example, paintersof a certain genre are situated, making the underlyingsimilarity layout easier to interpret (see Figure 11).

portrait

Figure 11. The text search allows the viewer to explore, forexample, the distribution of painters of portraits.

Additional controls are provided in the top right ofthe interface as drop-down menus to change the lay-out and data set of the visualization. Changing thedata set initiates a simple blending transition from onevisualization to the next. Switching between layoutstriggers a transition between the similarity map andthe timeline gradually moving the nodes into their newlocations. As the colours are based on similarity it ispossible to rediscover nodes from the similarity map inthe timeline view. The transition between the views is

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Figure 10. In the timeline view, influence edges result in a wave-like form indicating the propagation of philosophical ideasover many time periods. The more significant a philosopher is the larger is the resulting wave (below).

animated to allow the viewer to follow nodes betweenthe views and establish an understanding of temporaland topical interrelations. For example, by comparinghow these transitions unfold it is possible to visually ex-amine whether keyword-based similarities correspondwith time periods people were active in.

To learn more about a person, a detailed view withadditional information about the selected node is dis-played in the lower right portion of the window (seeFigure 12). A short description of the person is dis-played and accompanied with a visual depiction (e.g.,photo, painting, sculpture) of the person. Selecting thethumbnail opens the corresponding entry on Freebaseallowing the viewer to go to the data source and learnmore about a given person. The border of this detaildisplay uses the colour of the corresponding person’snode in the EdgeMaps visualization.

6.2 Visual Information Design

As an attempt to reduce visual clutter, only the nameof the person that is currently selected and the peo-ple associated with the selected person are displayed.

The nodes for the remaining people are dimmed anddo not have a label. This allows the viewer to focuson the current selection, but it also alleviates a label-occlusion problem that still occurs when many edges ofassociated nodes are displayed (see Figure 9, right). Itis possible to hover with the mouse over any node tomake occluded or hidden labels visible. To ensure aes-thetic proportions between circles and labels, the fontsize is set relative to the size of the circle. Further-more, only the surname of the person is used, which istypically unique and sufficiently known.

Legends are displayed to summarize the differentmappings that are used. For example, for the size ofcircles and types of edges, a legend for both layoutsis always displayed in the lower left of the screen (seeFigure 12, lower left). The two circle sizes displayedcorrespond to the smallest and largest nodes in the vi-sualization, giving a sense of the extent of significancebetween people. In the timeline view, years indicate thetemporal distribution along the time axis. The legendsare drawn in shades of light grey to avoid distractionfrom the information visualization.

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History

Title and Address Bookmarking

Search andView Controls

DetailLegend

Selection

Data Source

Sharing

Figure 12. The web-based interface of EdgeMaps ties in with common browser functions and use patterns on the Web.

6.3 Web Integration and Native Graphics

One goal behind realizing EdgeMaps as a web-basedsystem, was to align the interactivity with existing usepatterns supported by web browsers. As shown in Fig-ure 12, our prototype supports the browser’s history,bookmarking, and sharing features. The back button inweb browsers provides a common way of backtrackingon the Web allowing the surfer to return to a previouspage; it serves a similar function in the EdgeMaps tool,the viewer can use it to return to a previous state of thevisualization. The state of the visualization, i. e., thetype of layout, data set, selection, and search query, isencoded into the address as part of the fragment iden-tifier of the URL. In this way the interaction steps canbe added to the browser’s history, a given state canbe bookmarked or shared as a link via email, instantmessaging, or on social networks. We also adjust thetitle to reflect the current state of the visualization tomake the history, bookmarks, and link titles easy tounderstand. For example, when dragging the addressinto a rich-text document, modern browsers and wordprocessors automatically turn it into a hyperlink withthe corresponding title (see Figure 12, bottom left).

To draw interactive visualizations in the browser,there are several options. On the one hand there arebrowser plug-ins, such as Flash and Java, that are used

traditionally to provide advanced graphics and interac-tivity. On the other hand, new web standards—in par-ticular HTML5—are currently emerging that strive fornative support of interactive graphics in the browser.We are interested in exploring the browsers’ native,standards-based graphics capabilities and in not rely-ing on proprietary technologies. This approach hasalso the benefit that the tool would be viewable onmobile devices that do not support browser plugins(mainly Apple’s iOS devices). The two main optionsfor browser-native graphics are the Canvas element forbitmap drawing and SVG for scalable vector graphics.As native browser graphics are currently going throughrapid developments, we considered both approaches.To make a quick performance comparison between Can-vas and SVG, we have drawn 1000 random circles withdiffering opacities, positions, and sizes. In this exper-iment the Canvas element was about twice as fast interms of rendering than the SVG. While the output ofboth approaches looks identical, browser-based SVG al-lows for straightforward event handling, as it retains itsown database of drawn elements. In contrast, the Can-vas element requires custom picking logic for the objectsto be interacted with, which would lead to significantperformance overhead. Based on these considerationswe chose SVG in combination with the vector graphicsJavaScript library Raphael.27

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!! TIME LINE

LEGEND

SIGNIFICANCE

degree of influence1

47

INFLUENCE A B C

B influenced A, and B was influenced by C

BEAUVOIR

!! TIME LINE

LEGEND

SIGNIFICANCE

degree of influence1

47

INFLUENCE A B C

B influenced A, and B was influenced by C

CAMUS

DELEUZE

HUSSERL

NIETZSCHE

HEGELKANT

SARTRE

MARX

HEIDEGGER

DESCARTES

KIERKEGAARD

BEAUVOIR

Simone de Beauvoir (pronounced [sim!n d" bo#vwa$] in

French) (January 9, 1908 – April 14, 1986) was a Frenchauthor and philosopher. She wrote novels, monographs onphilosophy, politics, and social issues, essays,

biographies, and an autobiography in several volumes. She isnow best known for her metaphysical novels, including She

Came to Stay and The Mandarins, and for her 1949 treatiseThe Second Sex, a detailed analysis of women's oppressionand a foundational tract of contemporary...

!! TIME LINE

LEGEND

SIGNIFICANCE

degree of influence1

47

INFLUENCE A B C

B influenced A, and B was influenced by C

BADIOU

WHITEHEAD

SPINOZA

LéVI-STRAUSS

GUATTARI

NIETZSCHE

HEGEL

BERGSON

KANT

SARTRE

BLANCHOT

MERLEAU-PONTY

FOUCAULT

MAIMON

BEAUVOIR

DELEUZE

Gilles Deleuze (French pronunciation: [!il d"løz]), (18

January 1925 – 4 November 1995) was a French philosopherof the late 20th century. From the early 1960s until hisdeath, Deleuze wrote many influential works on philosophy,

literature, film, and fine art. His most popular books werethe two volumes of Capitalism and Schizophrenia:

Anti-Oedipus (1972) and A Thousand Plateaus (1980), bothco-written with Félix Guattari. His books Difference andRepetition (1968) and The Logic of Sense...

!! TIME LINE

LEGEND

SIGNIFICANCE

degree of influence1

47

INFLUENCE A B C

B influenced A, and B was influenced by C

CAMUS

GORDON

LéVINAS

GUATTARI

VATTIMO

DELEUZEDERRIDA

SARTRE

CAPUTO

RAWLS

STRAUSS

WITTGENSTEIN

BUBER

HEIDEGGER

BLANCHOT

MERLEAU-PONTY

FOUCAULT

RORTY

BEAUVOIR

ADORNO

BENJAMIN

SPIR

SCHOPENHAUER

SPINOZA

PASCAL

EMPEDOCLES

HERACLITUS

KANT

SCHELER

PARMENIDES

PLATO

NIETZSCHE

Friedrich Wilhelm Nietzsche (October 15, 1844 – August

25, 1900) (German pronunciation: [!f"i#d"$ç!v$lh%lm !ni#t&%]) was a nineteenth-century Germanphilosopher and classical philologist. He wrote critical

texts on religion, morality, contemporary culture,philosophy, and science, using a distinctive German language

style and displaying a fondness for metaphor and aphorism.Nietzsche's influence remains substantial within and beyondphilosophy, notably in existentialism and postmodernism....

Figure 13. Exploratory pivoting along philosophers’ influences from Beauvoir over Deleuze to Nietzsche.

7. REVEALING COMPLEXRELATIONSHIPS

By visualizing explicit and implicit relations, EdgeMapsallow the viewer to explore data patterns that are dif-ficult to see otherwise. In the following, we illustratesome of the new kinds of exploration that can be pur-sued and the types of insight that can be gained.

Pivotal Data Exploration. Limiting selections toone node at a time has interesting implications on theinteraction with the visualization. Activating one per-son draws the incoming and outgoing edges, highlightsthe respective nodes, and displays their names. As thelinked nodes are emphasized, the viewer is more likelyto follow the displayed edges and activate one of thelinked people. In a sense, exploring the visualization

along influence edges can be seen to be like pivotingthrough the data set around one person at a time. Eachstep triggers a novel visualization sharing the position-ing of the former state, yet providing a different influ-ence networks centred around the current node. Con-sider selecting, for example, the node of the philosopherBeauvoir, which is one of the smaller circles in the pe-riphery of the visualization (see Figure 13, top). Afterhaving selected her node it is now possible to followone of the philosophers that was influenced by her. Inthis case one could select Deleuze (bottom right) andafterwards a philosopher with a larger and more satu-rated node, in this case Nietzsche (bottom left). Thepath of exploration depends somewhat on serendipity,intuition, and interest, all of which are affected by theoverall visualization and the viewer’s inclinations.

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Figure 14. EdgeMaps reveal different patterns of how musicians influence each other. There is a difference betweenreaching members of similar genres (top left) versus reaching further across the similarity map (top right). Influential andinnovative musicians tend to show a spatial split between incoming and outgoing influences (bottom left and right).

Qualitative Differences in Influence. The in-fluence edges reaching across the view generate visualpatterns that can help develop a rich understanding ofa person’s influence. For example, the similarity mapof the musical artists’ data allows us to see the reachof a musician’s influence. Taking a closer look at theinfluence network of The Beatles shows that their in-fluence spans most regions of the similarity map exceptthose inhabited mostly by jazz, country, and rap singersat the bottom of the visualization (see Figure 1). Incontrast, the influence network of the country singerHank Williams appears much more constrained to hisimmediate neighbours (see Figure 14, top left). Fur-thermore, it is possible to compare the relative location

of nodes that are connected by incoming and outgoinginfluence edges. For instance, John Coltrane appearsto be influenced by musicians of similar genres, whilehis influence reached relatively far and wide across thesimilarity map (see Figure 14, top right). The influencenetworks of Jimi Hendrix and The Ramones are in eachcase located between the musicians they got influencedby and the singers and bands they influenced (see Fig-ure 14, bottom left and right). Taking into accountthat Jimi Hendrix is considered a pioneer of the elec-tric guitar and The Ramones are seen by many as thefirst punk-rock band, it makes intuitive sense that theirincoming and outgoing influences are disparate groupsof musicians on the similarity map.

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jazz

punk

Figure 15. The gradual colour changes in the time layout of musicians suggests a correlation between genres and timeperiods. This observation can be examined more closely by highlighting nodes matching search terms of genres.

impression

abstract

Figure 16. In the painters’ timeline view, there is a slightly higher concentration of blue and purple tones on the right.Searching for terms indicating artistic movements confirms the temporal development of impressionist and abstract art.

Time-Similarity Comparisons. By switchingbetween time and similarity layouts it appears as if timeperiods and genres of musicians have a relatively highcorrelation. This hunch is further supported consid-ering the colour distribution of nodes along the timeaxis (see Figure 15, top). The nodes appearing in thebottom left in the similarity map in shades of purple,are mostly located on the left side of the timeline, i. e.,in the first half of the last century. On the flip side,the nodes that appeared in tones of green in the topright of the similarity map are located towards the rightof the timeline. Examining the distribution of nodeshighlighted for the search terms ‘jazz’ and ‘punk’ fur-ther supports this observation (see Figure 15, middleand bottom). The painters’ timeline does not indicatesuch a strong correlation between similarity map lo-

cations with time periods, however, searching for ‘im-pression’ and ‘abstract’ indicates relatively discrete in-tervals along the time axis (see Figure 16). The moreheterogenous colour distribution is likely due to the in-clusion of art forms in the similarity keywords that arefairly independent from artistic movements.

8. DISCUSSION

The design and implementation of EdgeMaps consti-tute a first step towards integrating explicit and implicitdata relations. While the idea of integrating graphdrawing and spatialization techniques is promising, wediscuss several limitations and challenges that suggestopportunities for more research on the visualization ofcomplex data sets featuring diverse relations.

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Edge congestion. We have argued that displayingedges for only one node, i. e., philosopher, painter, ormusician, at a time allows for novel exploration meth-ods and avoids some of the edge congestion problemsof larger NLDs. However, once a node has a wide anddense influence network, similar edge congestion chal-lenges occur. It would be interesting to examine howlens and bundle techniques6,7 could be combined withvisualizations that use a layout representing implicitrelations with edges for explicit relations.

Overlapping nodes. With growing informationspaces, the challenge is not only edge congestion, butthe overall perceptual scalability of visualizations withmore and more overlapping nodes. In the timeline view,it would be possible to position the nodes vertically, forexample, ordered by significance, i. e., size. The inter-est map currently places smaller nodes around largernodes with the same position. In order to support largenumbers of overlapping nodes, it is possible to indicatespatial togetherness using bubble sets.28

Aggregating nodes. Another promising ap-proach is to introduce aggregation that can simplifythe graph visualization and provide higher-level per-spectives.17,29 Considering the time and similarity lay-outs of EdgeMaps, nodes could be aggregated alongtemporal or topical proximity. The resulting influencenetworks of clusters would allow for higher level explo-ration of mutual influences of groups of philosophers,painters, or musicians. For example, the viewer couldexplore how painters of the Renaissance influenced theimpressionists, or how jazz and blues singers affectedelectronic and new wave musicians.

Data set. The data sets used to illustrate the po-tential of EdgeMaps are limited both in size and alsocultural scope that is centred around North Americaand Europe. This bias likely reflects the demographicsof the Freebase community. It would be interesting touse the visualization as a starting point to add miss-ing connections, scrutinize the keywords, and add dataentries that are beyond the cultural horizon. Besidesvisualizing historic personalities, we are interested inexamining other data sets such as citation networks,migration patterns, and international trade.

Spatialization. While we used an existing MDSalgorithm,26 it would be beneficial to explore its param-eters and, for example, consider planes with dynamicrectangular shapes besides squares as it is realistic torely on windows of fixed aspect ratios. The difficultyto interpret the meaning of position and proximity isalleviated with the display of influence edges, yet fur-ther exploration is necessary to find other techniques

that make the output of MDS algorithms more accessi-ble and meaningful. One of the ideas that arose duringthis work is to label regions in the MDS plane based onrepresentative keywords that are more common amongnodes that are positioned closer to each other. Besidesthe aim of making the MDS layout more comprehen-sible, it could be useful to create a flexible MDS al-gorithm allowing the viewer to change how items arepositioned. The great challenge for this would be tomake this algorithm interactive.

Folding time. The timeline features an irregularscaling that is due to removing spacing between nodesin favour of larger information density. While the tex-tual time legend indicates this temporal folding, theadded grid lines visually expose the scaling. Ideally theviewer would be able to adjust time foldings accord-ing to their interests and exploration steps. Currentlya person is represented as a single point on the time-line, which makes it difficult to represent their lifetimeduration or longevity of active influence. It would beinteresting to represent the actual lifespans and activeyears of philosophers, painters, and musicians.

Layouts. Supplementing the graph visualizationwith meaningful node positions led to the creation ofnovel visual representations that expose temporal andtopical qualities of data connections. Besides time andkeywords, many more dimensions can be utilized as im-plicit relations for underlying layouts. For example, po-sitions in a hierarchy or geographic space could be usedfor node placement. It is also feasible to combine twotypes of similarities for the spatial dimensions of theplane. For example, a one-dimensional MDS projectioncould be juxtaposed with the temporal distribution ofnodes. In addition to enhancing the understanding ofinfluence connections, such a hybrid layout could helpthe viewer to compare time relations with similaritiesbased on interests, movements, or genres. As artisticmovements can be viewed as correlations between timeand style, a corresponding layout could be very helpful.

Insight and experience. Having applied theEdgeMaps visualization to three data sets allowed usto closely examine the insights and experiences thatcan be supported. However, we have only anecdotalevidence for the potential of visualizing explicit andimplicit relations this way. It would be beneficial tostudy how domain-experts would explore the data setsusing EdgeMaps. Particularly, we would be interestedin the role of pivoting around individual nodes to gaina bottom-up understanding of a complex data set. Itmay be interesting to compare how domain experts ex-perience the visualization of a given data set in contrastto viewers that are unfamiliar with the data.

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9. CONCLUSION

As information spaces grow in size and complexity,there is a growing need for visual representations thathelp us make better sense of diverse data relations andpatterns. With this work we have approached this chal-lenge by making the following contributions:

• A novel information visualization technique thatcombines implicit relations (topical and temporalsimilarity) with explicit relations (influences).

• Detailed discussion of the design considerationsconcerning visual representations and interactiontechniques of the EdgeMaps visualization.

• Implementation of a web-based system incorporat-ing basic use patterns on the Web such as browser’shistory, bookmarks, and link sharing.

• Application to three case studies exploring dataabout personalities from philosophy, art, and musicexamining the types of novel insights and uses.

The EdgeMaps technique represents implicit rela-tions as the underlying layouts for node-link diagramsin which nodes stand for people, and edges for explicitinfluence connections between them. By constrainingthe selection to one node at a time, it is possible to visu-ally distinguish between incoming and outgoing edges.Novel visual patterns resembling the aesthetics of fire-works and waves may not only look more appealing,they also allow for a closer examination of influence dif-ferences. In contrast to the notorious yarn-ball effectof dense graph visualizations, we have suggested thatan interactive pivotal exploration along edges betweennodes may better allow viewers to grasp network struc-ture than complicated overviews. While much morework is necessary to help us better explore and under-stand complex information spaces, we see EdgeMaps asa promising step towards this effort.

ACKNOWLEDGEMENTS

Funding was provided by SMART Technologies,NSERC, iCORE, and NECTAR.

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