|
Items in cart [1] |
Questions? Call 888-624-8373 |
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 121
Colloquium
Searching for intellectual turning points: Progressive
knowledge domain visualization
Chaomei Chen*
College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875
This article introduces a previously undescribed method progres-
sively visualizing the evolution of a knowledge domain's cogitation
network. The method first derives a sequence of cocitation net-
works from a series of equal-length time interval slices. These
time-registered networks are merged and visualized in a pan-
oramic view in such a way that intellectually significant articles can
be identified based on their visually salient features. The method
is applied to a cogitation study of the superstring field in theoretical
physics. The study focuses on the search of articles that triggered
two superstring revolutions. Visually salient nodes in the pan-
oramic view are identified, and the nature of their intellectual
contributions is validated by leading scientists in the field. The
analysis has demonstrated that a search for intellectual turning
points can be narrowed down to visually salient nodes in the
visualized network. The method provides a promising way to
simplify otherwise cognitively demanding tasks to a search for
landmarks, pivots, and hubs.
The primary goal of knowledge domain visualization (KDViz)
is to detect and monitor the evolution of a knowledge domain
(1~. Progressive knowledge domain visualization is specifically
concerned with techniques that can be used to identify temporal
patterns associated with significant contributions as a domain
advances.
Many aspects of a scientific field can be represented in the
form of a scientific network, such as scientific collaboration
networks (2), social networks of coauthorship (3), citation
networks (4), and cocitation networks (5~. Scientific networks
constantly change over time. Some changes are relatively mod-
erate; some can be dramatic. Understanding the implications of
such changes is essential to everyone in a scientific field.
Researchers have been persistently searching for underlying
mechanisms that may explain various changes and patterns in
scientific networks. On the other hand, this is an ambitious and
challenging quest because of the scale, diversity, and dynamic
nature of scientific networks that one has to deal with. In this
article, we introduce a previously undescribed method designed
to reduce some of the complexities associated with identifying
key changes in a knowledge domain. We focus on cocitation
networks, although we expect that the method is applicable to a
wider range of networks.
The key elements of the method draw their strength from a
divide-and-conquer strategy. A time interval is divided into a
number of slices, and an individual cocitation network is derived
from each time slice. The time series of networks are merged.
Major changes between adjacent slices are highlighted in a
panoramic visualization of the merged network. The primary
motivation of the work is to simplify the search for significant
papers in a knowledge domain's literature so that one can search
for visually salient features, such as landmark nodes, hub nodes,
and pivot nodes, in a visualized network. The entire progressive
visualization process is streamlined and implemented in a com-
puter system of the author called CITESPACE.
www.pnas.org/cgi/doi/10.1073/pnas.0307513100
The rest of this paper is organized as follows. We first review
prior studies of the growth of a knowledge domain and then
identify the key issues to be addressed by our method. The
progressive visualization method is described and illustrated
with an example in which we identify intellectual turning points
in the field of superstring in theoretical physics. Identified
articles associated with visually salient features are validated
with the leading scientists in the field of superstring.
Related Work
Two strands of research are relevant. The focus of our research
can be expressed in two key questions. How does a scientific field
grow? What has been done for visualizing temporal patterns,
especially in relation to network evolution?
Scientific Revolutions
The most widely known model of science is Thomas Kuhn's
Structure of Scientific Revolutions (6), in which science is char-
acterized by transitions from normal science to science in crisis
and from crisis to a scientific revolution. Kuhn's theory suggests
that scientific revolutions are a crucial part of science. The
notion of paradigm shift is widely known in virtually all scientific
disciplines. Kuhn's model has generated profound interest in
detecting and monitoring paradigm shifts through the study of
temporal patterns in cocitation networks. Small (7) identified
and monitored the changes of research focus in collagen research
in terms of how clusters of most cocited articles change over
consecutive years. Small's study predated many modern visual-
ization techniques. However, the representations of cocitation
clusters were isolated from one year to another; significant
temporal patterns or transitions may go unnoticed if they fall
between the clusters from different years.
In our earlier work (8), we used animated visualization
techniques to reconstruct citation and cocitation events in their
chronological order so that one can examine the growth history
of a domain in a broader context in a similar way to how we play
a video in a fast-forward mode. The animated visualization
enabled us to identify paradigm-like clusters of cocited articles
corresponding to significant changes in the field of superstring,
the same topic we will revisit with our method, but the visual
features of some of the groundbreaking articles were not distinct
enough to lend themselves to a simple visual search. Our earlier
methodology did not include time slicing, multiple thresholding,
and merging. One of our main objectives is therefore to improve
visualization techniques so that groundbreaking articles can be
characterized by distinguishable visual features.
This paper results from the Arthur M. Sackler Colloquium of the National Acaclemy of
Sciences, "Mapping Knowledge Domains," held May 9-11, 2003, et the Arnold and Mabel
Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA.
Abbreviation: KDViz, knowlecige domain visualization.
*E-mail: chaomei.chen~cis.cirexei.edu.
C) 2004 by The National Academy of Sciences of the USA
PNAS 1 April 6, 2004 1 vol. 101 1 suppl. ~ 1 5303-5310
OCR for page 122
The concept of a research front is also relevant to how science
grows. A research front consists of transient clusters of most
recently cited works in the literature of a scientific field (91. A
research front represents the state of the art of a field, and
research fronts move along with the underlying scientific field as
new articles replace existing articles.
The recent interest in complex network analysis is a potentially
fruitful route to improve our understanding of scientific net-
works as well as general networks (10~. Studies in complex
network analysis, especially in relation to small-world and scale-
free networks, focus on two broad issues, namely topological
properties and generative mechanisms of networks. Various
growth models such as preferential attachment (10-12) have
been developed in the study of network evolution. However,
much of the work has concentrated on abstract network repre-
sentations rather than on concrete networks and their practical
implications. We emphasize the integral role of the semantics
of such networks in understanding the profound dynamics of
network evolution. We expect that the progressive method
described in this article can provide a useful instrument for
examining the evolution of a scientific network, and that the
concrete example of network evolution can lead to insights into
a broader range of networks.
Visualizing Temporal Patterns
A good example of visualizing thematic changes in a collection
of text documents is THEMERIVER (13), which uses the metaphor
of a thematic river to depict temporal changes of word frequen-
cies. An intensified theme can be identified if one can detect
increasingly widened word frequency streams. This is a relatively
straightforward task, given that one needs only to tell how much
the width of a stream changes over time. In contrast, it is often
much more complicated to detect temporal patterns in higher-
dimensional data or higher-order relationships.
A number of methods such as INDSCAL (14), PROCRUSTES
analyses (15), and thin-plate splines and deformation analysis
(16) can be used to compare dimensional representations.
PROCRUSTES analysis, for example, aligns two configurations by
stretching and rotating operations so that the remaining differ-
ences are where the two configurations really differ. Similarly,
thin-plate display renders the difference between two configu-
rations as a deformed plate. The degree of the deformation
indicates the extent to which the two configurations differ. Such
methods are efficient in detecting local and short-range discrep-
ancies between two almost identical configurations, but the
performance degrades if the discrepancies are long range in
nature or a substantial part of the configurations is involved. In
KDViz, we need to consider both short- and long-range changes
between two adjacent snapshots of a domain, although few
empirical studies have examined these techniques in the context
of KDViz.
A network can change over time in various ways and can
change its topology by adding new nodes and links as well as
removing existing nodes and existing links. A network can also
change the intrinsic attributes of its nodes and links; for example,
citations of articles in a cocitation network tend to increase over
time.
Much of the existing approaches to visualizing the evolution
of a network falls into one of two categories: the slide-show and
panorama approaches. Just like in a slide show, the former aims
to highlight the changes as the viewer moves forward, sometime
back and forth, in a time series of snapshots. The latter aims to
pack synthesized temporal changes into a single image.
The slide-show approach has several advantages, including
being easy to implement and flexible to use. This approach often
provides additional visual aids to help viewers identify changes
between adjacent snapshots. Recent examples include the visu-
alization of how a discourse evolves as a network of words (17)
5304 1 www pnas org/cgi/doi/ 10.1 073/pnas.03075 13100
and the visualization of semantic structures across different time
planes (18~. However, research in perceptual cognition has
shown that comparing two images back and forth can be
cognitively vey demanding and prone to error.
The panorama approach aims to depict temporal as well as
spatial changes in such a way that viewers can detect a trend or
a pattern by studying a single image. This approach could
minimize the disturbance to the viewer's mental model (19~.
Related work in this area includes incremental graph drawing
(20) and the timed network display function in PAJEK (21~. Our
earlier work on using animated visualization techniques to depict
temporal changes in a cocitation network also belongs to this
category (8~.
Progressive Visualization Issues
A progressive visualization method aims to visualize the evolu-
tion of a network over time. The following three issues need to
be addressed for visualizing time-sliced networks: (i) Improving
the clarity of individual networks; (ii) highlighting transitions
between adjacent networks; and (iii) identifying potentially
important nodes.
The first issue is concerned with the clarity of individual
networks' representations. One of the major aesthetic criteria
established by research in graph drawing is that link crossings
should be avoided whenever possible. A network visualization
with the least number of edge crossings is regarded as not only
aesthetically pleasing but also more efficient to work with in
terms of the performance of relevant perceptual tasks (22~. The
number of link crossings may be reduced by pruning various links
in a network. Minimum spanning trees and Pathfinder network
scaling are commonly used algorithms. The major advantages
and disadvantages of these scaling techniques are further ana-
lyzed below.
The second issue is concerned with progressively merging two
adjacent networks, so that one can identify which part of the
earlier network is persistent in the new network, which part of
the earlier network is no longer active in the new network, and
which part of the new network is completely new. Much of the
novelty of our method is associated with the way we address this
Issue.
The third issue is concerned with the role of visually salient
features in simplifying search tasks for intellectual turning
points. Visually salient nodes include landmark nodes, pivot
nodes, and hub nodes.
Issue 1: Improving the Clarity of Networks
Cocitation networks often have a vast number of links, and
displaying links indiscriminately is the primary cause of clutter.
There are two general approaches to reduce the number of links
in a display: threshold- and topology-based approaches. In the
threshold-based approach, the elimination of a link is deter-
mined solely by whether the link's weight exceeds a threshold. In
contrast, in a topology-based approach, the elimination of a link
is determined by a more extensive consideration of intrinsic
topological properties; therefore, such approaches tend to pre-
serve certain topological intrinsic properties more reliably, al-
though the computational complexity tends to be higher.
Pathfinder network scaling is originally developed by cognitive
scientists to build procedural models based on subjective ratings
(23-25~. It uses a more sophisticated link-elimination mecha-
nism compared to minimum spanning tree (MST) and can
remove a large number of links and retain the most important
ones. Given a network, one can derive a unique Pathfinder
network that contains all of the alternative MSTs of the original
network. MST is increasingly a strong candidate in a series of
KDViz studies (8, 26-28).
The goal of Pathfinder network scaling, in essence, is to prune
a dense network. The topology of a Pathfinder network is
Chen
OCR for page 123
determined by two parameters, r and q. The r parameter defines
a metric space over a given network based on the Minkowski
distance so that one can measure the length of a path connecting
two nodes in the network. The Minkowski distance becomes the
familiar Euclidean distance when r = 2. When r = x, the weight
of a path is defined as the maximum weight of its component
links, and the distance is known as the maximum value distance.
Given a metric space, a triangle inequality can be defined as
follows,
Wij C (ink W nkNk _ ~~/r'
where wit is the weight of a direct path between i and j,
wnknk + ~ is the weight of a path between nk and nk + I, for k =
1, 2, . . ., m. In particular, i = no andj = nk. In other words, the
alternative path between i and j may go all the way around
through nodes no, n2, . . ., nk, so long as each intermediate links
belong to the network.
If wit is greater than the weight of alternative path, then the
direct path between i and j violates the inequality condition. of an underlying phenomenon can be informatively stitched
Consequently, the link ij will be removed, because it is assumed together.
that such links do not represent the most salient aspects of the
association between the nodes i and j.
The q parameter specifies the maximum number of links that
alternative paths can have for the triangle inequality test. The
value of q can be set to any integer between 2 and N- 1, where
N is the number of nodes in the network. If an alternative path
has a lower cost than the direct path, the direct path will be
removed. In this way, Pathfinder reduces the number of links
from the original network, whereas all of the nodes remain
untouched. The resultant network is also known as a minimum-
cost network.
The strength of Pathfinder network scaling is its ability to
derive more accurate local structures than other comparable
algorithms, such as multidimensional scaling and minimum
spanning tree. However, the Pathfinder algorithm is computa-
tionally expensive; the published algorithm is in the class of
O(N4~. Viz approaches built on the Pathfinder network
scaling algorithm have a potential bottleneck if one needs to deal
with large networks. The maximum pruning power of Pathfinder
is achievable with q = N- 1 and r = or; not surprisingly, this is
also the most expensive one, because all of the possible paths
must be examined for each link. In addition, the algorithm
requires a large amount of memory to store the intermediate
distance matrices. This is the first of the three issues our method
is to deal with. The method follows a divide-and-conquer
strategy.
Landmark node
large races
-_'
Hub node
Urge degree
~iYC,t node
exclus~se~ is of casters
or newton pawns
....
Fig. 1. Three types of visually salient nodes in a cocitation network.
Issue 2: Merging Heterogeneous Networks
The second issue identified above is concerned with progres-
sively merging two temporally adjacent networks. Depending on
the nature of a knowledge domain, networks to be merged could
be heterogeneous as well as homogeneous in terms of intrinsic
topological properties and additional attributes of nodes and
links. For example, intellectual structures of a knowledge do-
main before and after a major conceptual revolution are likely
to be fundamentally different as new theories and evidence
become predominant. Cocitation networks of citation classics in
a field are likely to differ from cocitation networks of newly
published articles. The key question is, what is the most infor-
mative way to merge potentially diverse networks?
A merged network needs to capture important changes over
time in a knowledge domain's cocitation structure. We need to
find when and where the most influential changes took place so
that the evolution of the domain can be characterized and
visualized. Few studies in the literature investigated network
merge from a domain-centric perspective. The central idea of
our method is to visualize how different network representations
Chen
Issue 3: Visually Salient Nodes in Merged Networks
The third issue addressed by our method is concerned with the
identification of potentially important articles in a cocitation
network. The importance of a node in a cocitation network can
be quickly identified by the local topological structure of the
node and by additional attributes of the node. We are particu-
larly interested in three types of nodes: (i) landmark, (~ii') hub, and
(iii) pivot nodes (see Fig. 1~.
A landmark node has extraordinary attribute values. For
example, a highly cited article tends to provide an important
landmark regardless of how it is cocited with other articles.
Landmark nodes can be rendered by distinctive visual-spatial
attributes such as size, height, or volume. A hub node has a
relatively large node degree; a widely cocited article is a good
candidate for significant intellectual contributions. A high-
degree hub-like node is also easy to recognize in a visualized
network. Both landmark and hub nodes are commonly used in
network visualization. Although the concept of pivot nodes is
available in various contexts, the way they are used in our method
is previously undescribed. Pivot nodes are joints between dif-
ferent networks; they are either the common nodes shared by
two networks or the gateway nodes that are connected by
internetwork links. Pivot nodes have an essential role in our
method.
Methods
The method includes the following procedural steps: time slicing,
thresholding, modeling, pruning, merging, and mapping. A1-
though pruning is not always necessary, it is a potentially
valuable option when dealing with a dense network. All steps are
implemented in CITESPACE.
Procedure. The input to CITESPACE iS a set of bibliographic data
files in the field-tagged Institute for Scientific Informationt
Export Format. The outputs of CITESPACE include visualized
cocitation networks; each network is shown in a separate inter-
active window interface.
Time Slicing. The entire time interval can be sliced into equal-
length segments. The length of each segment can be as short as
a year or as long as the entire interval. If appropriate data
tThese data are extracted from Science Citation /nclex Expancled [Institute for Scientific
Information, inc. (ISI), Philadelphia, PA; Copyright ISI]. Ail rights reserved. No portion of
these clata may be reproduced or transmitted in any form or by any means without the
prior written permission of ISI.
PNAS 1 April 6, 2004 1 vof. 101 1 suppl. ~ 1 5305
OCR for page 124
become available, it is possible to slice the data thinner to make
monthly or weekly segments. Currently, sliced segments are
mutually exclusive, although overlapping segments could be an
interesting alternative worth exploring.
Thresholding. Citation and cocitation analysis typically sample the
most highly cited work, the cream of crop, with a single constant
threshold. However, a single constant threshold is a crude
sampling mechanism if the citation patterns over an extended
period are being considered. By default, both citations and
cocitations are calculated within each time slice, as opposed to
across all time slices.
Time slicing provides the flexibility to tailor a threshold more
closely to the characteristics of citation and cocitation activities
in each individual time slice. This flexibility is expected to reduce
the bias associated with a single one-size-fits-all threshold. One
can even compare and merge two very different networks within
this framework, for example, a network of articles from Nobel
Prize-winning scientists and a network of technical reports. The
key questions are: what is the common ground between two
networks? How can one extract insights into the internetwork
relationship from such common ground? A flexible threshold
configuration can find a common ground more easily.
The cocitation network in a given time slice is determined by
three thresholds: citation, cocitation, and cosine coefficient
thresholds. In CITESPACE, the user needs to select desired
thresholds for three specific time slices, namely the beginning,
middle, and ending slices. CITESPACE automatically assigns in-
terpolated thresholds to the remaining slices. In practice, the
user starts with an arbitrary threshold configuration and then
adjusts thresholds accordingly based on the reported statistics
such as the citation population and the numbers of nodes and
links in a network.
In the citation world, articles are not created equal. Some
articles have much more than their fair share of citations, some
have less, and some have none at all. Citations depend on many
underlying factors. For example, success breeds success; a highly
cited article is likely to receive more citations than a currently
less frequently cited article. To detect intellectual turning points,
we are particularly interested in articles that have rapidly grow-
ing citations. In the following superstring example, we use a
simple model to normalize the citations of an article within each
time slice by the logarithm of its publication age, the number of
years elapsed since its publication year. The rationale is to
highlight articles that increased most in the early years of
publication. More sophisticated models can be derived based on
citation distribution models of a given dataset and a model of the
growth and decay of scientific citations (29~. Building such
models is significant and challenging in its own right.
coefficients, cCcosineti'j] = ccti, ji/sqrt~cti], cad), where ccti, j] is
the cocitation count between documents i andj, and cti] and cot]
are their citation counts, respectively. The user can specify a
selection threshold for cocitation coefficients; the default value
is 0.15.
Alternative measures of cocitation strengths are available in
the information science literature, such as Dice and Jaccard
coefficients. In earlier studies, we used Pearson's correlation
coefficients. Recently, researchers began to examine how Pear-
son's correlation coefficients transform the underlying structure
of a cocitation network (30), but available evidence is still
inconclusive (31, 32~. Although the impact of various cocitation
metrics on the resultant network visualizations is worth pursuing,
the topic is beyond the scope of this article.
Pruning. Effective pruning can reduce link crossings and improve
the clarity of the resultant network visualization. CITESPACE
supports two common network-pruning algorithms, namely
Pathfinder and minimum spanning tree. The user can select to
prune individual networks only or the merged network only or
to prune both. Pruning increases the complexity of the visual-
ization process. In the following section, visualizations with local
pruning and global pruning are presented.
In this article, we concentrate on Pathfinder-based pruning.
To prune individual networks with Pathfinder, the parameters q
and r were set to Nk - 1 and =, respectively, to ensure the most
extensive pruning effect, where Nk is the size of the network in
the kth time slice. For the merged network, the q parameter is
(>Nk)-1,fork=1,2.
Merging. The sequence of time-sliced networks is merged into a
synthesized network, which contains the set union of all nodes
ever to appear in any of the individual networks. Links from
individual networks are merged based on either the earliest
establishment rule or the latest reinforcement rule. The earliest
establishment rule selects the link that has the earliest time
stamp and drops subsequent links connecting the same pair of
nodes, whereas the latest reinforcement rule retains the link that
has the latest time stamp and eliminates earlier links.
By default, the earliest establishment rule applies. The ratio-
nale is to support the detection of the earliest moment when a
connection was made in the literature. More precisely, such links
mark the first time a connection becomes strong enough with
respect to the chosen thresholds.
Mapping. The layout of each network, either individual time-
sliced networks or the merged one, is produced by using Kamada
and Kawai's algorithm (33~. The size of a node is proportional
to the normalized citation counts in the latest time interval.
Landmark nodes can be identified by their large discs. The label
Modeling. By default, cocitation counts are calculated within each size of each node is proportional to citations of the article, thus
time-sliced segment. Cocitation counts are normalized as cosine larger nodes also have larger-sized labels. The user can enlarge
Table 1. Time slicing and threshold settings for a set of small networks, where fc is the
citation frequency threshold and fcc is the cocitation frequency threshold
Time slices fc fcc Cite space size Top cited Sample, %
1985-1987 3 1 604 16 2.65 58
1988-1990 10 3 2,740 15 0.55 30
1991-1993 50 7 12,214 18 0.15 62
1994-1996 60 10 16,147 19 0.12 53
1997-1999 80 10 19,716 20 0.10 60
2000-2002 85 15 22,449 20 0.09 54
2003 25 10 9,594 13 0.14 34
Total (unique) 83,464 121 (82) Mean 0.54 Total 351
cc, cocitation.
5306 1 www.pnas.org/cgi/doi/10.1073/pnas.0307513100
Chen
OCR for page 125
# :~' ~ ~
hi' _i~ 1
~SEIBERG N19~`fB00KPO
ROSEN Atf9.
at'.
.~11°. ~ EM
X~
~OLCHINSK1 J1 - cs'7Sp~rd
·~5 .
PREEN MEl19~17
~ Yt~
CAN DE LAS P 1985
V258 P46
WITTEN E 1991
V44 P31
~ Turning points
FRIEDAN D1986 ~ Pivot points
V27 P93
· 1985-1987 51 1988-1990 121 1991-1993 51 1994-1996 151 1997-1999 O 2000-2002 ~ 2003
Fig. 2. An 82-node merged network without global pruning. See a color version at www.pages.drexel.edu/~cc345/citespace/Figure 2.png.
font sizes at will, and both the width and the length of a link are
proportional to the corresponding cocitation coefficient. The
color of a link indicates the earliest appearance time of the link
with reference to chosen thresholds.
Visually salient nodes such as landmarks, hubs, and pivots are
easy to detect by visual inspection. CITESPACE currently does not
include any algorithms to detect such nodes computationally.
Instead, the visual effect is a natural result of slicing and merging,
although additional computational metrics may enhance the
visual features even further. A useful computational metric
should reflect the degree of a node, and it should also take into
account the heterogeneity of the node's links. The more dissim-
ilar links a node connects to others, the more likely the node has
a pivotal role to play. In the following example, we consider only
nodes that have a degree of 10 or higher for visual inspection.
Superstring. The method is applied to the visualization of how
cocitation networks of superstring in theoretical physics evolved
over time. Two superstring revolutions are documented over the
last two decades: one in the mid-1980s and one in the mid-199Os
(34~. We reported animated visualizations of the superstring
cocitation networks by using a single constant citation threshold,
and Pearson's correlation coefficients were used to measure the
strength of each cocitation link. The changes of the cocitation
network were animated by the growing height of a citation bar
and as a state transition process. Although key articles to the
revolutions were identifiable in the resultant animations, they
did not quite lend themselves to a simple visual inspection. We
expect that the progressive visualization method can make it
more easily to identify intellectual turning points by visual
inspection. The superstring dataset in this study is updated to
include citation data between 1985 and 2003.
Visualized networks were validated by the leading scientists in
the field of superstring. We showed the merged map, without
pruning, to John Schwarz (California Institute of Technology,
Pasadena) and Edward Witten (Princeton University, Prince-
ton). Schwarz is the coauthor of the article that triggered the first
superstring revolution; Witten has written a number of highly
cited articles on superstring and is also the top-ranked physicist
in a list of the 1,000 physicists most cited between 1981 and 1997.
The list was compiled by the Institute for Scientific Information,
who were asked to explain the nature of intellectual contribu-
tions identified by pivot points and hubs in the networks.
The 19-year time interval was sliced into six 3-year segments,
starting from 1985-1987 and ending with 2000-2002, plus a
1-year segment for 2003. Two sets of results were generated from
Chen
two separate runs: one used relatively higher-threshold settings,
which resulted in small networks (Table 1~; the other used
lower-threshold settings for larger networks (Table 2~. Two
versions of the larger network are shown: one without global
pruning (Fig.3) and the other with global pruning (Fig.4~. Links
were color-coded by the earliest establishment rule. Darker
colors indicate links from earlier time slices, whereas lighter
colors indicate links from more recent slices. Networks in
individual time slices are not shown due to page limitations.
Results
Table 1 shows the size of the cite space and details of individual
networks and the merged network. The size of the cite space in
a given time slice is the number of articles that have at least one
citation within the given time slice; the size is generally increas-
ing over time. The size for 2003 is smaller, because the 2003 data
are still incomplete. The merged network contains 82 articles,
and various pivot points are evident at a glance (Fig. 2~.
Table 2 shows the threshold setting for a sequence of larger
networks. The cocitation network in each time slice represents
approximately the top 1% most cited articles. The merged
network contains 647 unique articles, which collectively made
1,097 appearances in these time slices. In other words, 41% of
articles appeared in more than one time slice. The locally pruned
version of the merged network is shown in Fig. 3; the globally
pruned version is shown in Fig. 4.
As shown in Fig. 3, color-coded links in effect partitioned the
merged network into several major clusters of articles. Clusters
of the same color represent cocitations made within the same
time slice. More importantly, as we expected, within-cluster
cocitation links are evidently more common than between-
cluster links. A strongly clustered network also makes it easy to
identify pivot nodes and between-cluster links. Six structurally
strategic nodes are identified in Fig. 3, including the 1984
Green-Schwarz article, which triggered the first superstring
revolution. However, the 1995 Polchinski article that triggered
the second superstring revolution was not obvious in the dense
visualization; Polchinski introduced the fundamental concept of
D-branes in that article.
The 1984 Green-Schwarz article is a typical pivot node; it is
the only contact point between two densely connected clusters
in blue (1985-1987~. It was this article that sparked the first
superstring revolution, the famous 1984 Green-Schwarz anom-
aly cancellation paper. Friedan's 1986 article is a distinct pivot
node connecting blue (1985-1987), pink (1988-1990), and green
clusters (1991-1993~. Witten's 1986 article is a pivot between a
PNAS 1 April 6, 2004 1 volt. 101 1 supply. ~ 1 5307
OCR for page 126
Table 2. Time slicing and threshold settings for a set of larger networks, where fc is the
citation frequency threshold and kc is the cogitation frequency threshold
Time slices fc fcc Cite space size Top cited Sample, % cc (cosine—0.15)
1985-1987 2 1 604 39 6.46 229
1988-1990 4 3 2,740 114 4.16 283
1991-1993 15 7 12,214 200 1.64 1,263
199~1996 20 10 16,147 229 1.42 895
1997-1999 25 10 19,716 223 1.13 956
2000-2002 30 15 22,449 180 0.80 486
2003 10 10 9,594 112 1.17 131
Total (unique) 83,464 1,097 (647) Mean 2.4 Total 4,243
cc, cocitation.
blue cluster (1985-1987) and a yellow cluster (2000-2002~. Fig.
3 also contains a couple of smaller clusters that are completely
isolated from the main super cluster. Small clusters in red (2003)
indicate the candidates for emerging clusters. We were able to
find Polchinski's 1995 article in a smaller-sized merged network,
but the article must be overwhelmed by the 4,000 strong links of
the larger network. Nevertheless, the quality of the visualized
network is promising: intellectually significant articles tend to
have topologically unique features.
Articles by Maldacena, Witten, and Gubser-Klebanov-
Polyako, located toward the top of the major network compo-
nent, were all published in 1998. When we asked Witten to
comment on an earlier version of the map, in which citation
counts were not normalized by years since publication, he
indicated that the Green-Scharwz article is more important to
the field than the three top-cited ones, and that the earlier
articles in the l990s appeared to be underrepresented in the map.
The apparent mismatch between citation frequencies of nodes
and their importance judged by domain experts was partially
corrected in the network shown in Fig. 2. Witten's comments
HAWKING SW 1975 ~~ _
V43 P1 99
WITTEN E 1991
V44 P314.
-
FRIEDAN D 1986 ~
V271 P93 \
raised an important question: is it possible that an intellectually
significant article may not always be the most highly cited?
Fig. 4 shows the merged network pruned by Pathfinder; the
pruned version contains fewer links than the version in Fig. 3.
Much of the within-cluster links is reduced to links between
cluster centers and other cluster members. Links between non-
center members are essentially removed. The overall structure is
simpler and easier to explore. In addition, the number of link
colors attached to a node distinguishes a pivot node from a
nonpivot node. If a node connects to other nodes through links
in a single color, it is not regarded as a pivot node, because it does
not imply intellectual transitions over time. In contrast, if a node
joins several different-colored links, it is a good candidate for an
intellectual turning point, because if paths connecting articles in
different clusters must go through a pivot point, the pivot point
is likely to have a unique position in the literature.
The Green-Schwarz article is located toward the center of the
visualization; it joins links from four different time slices. The
1995 article by Candelas et al. is similar in terms of the link
colors. According to Institute for Scientific Information's Sci-
:~ At:
GREEN MB, SCHWARZ
JH 11984 V149 P117
~~ ,. . .._
~ IRONY 0~00~3M83
3= ~ J1998Y2P231
::,
:::
I:
WITTEN E 1986
V268 P253
DINE M 1985
V156 P55
lo, Turning points
Pivot points
~ 1985-1987 · 1988-1990 O 1991-1993 C1 1994-1996 [3 1997-1999 ~ 2000-20Q2 ~ 2003
Fig. 3. A 624-node merged network without global pruning. See a color version at: www.pages.drexel.edu/~cc345/citespace/Figure3.png.
5308 1 www.pnas.org/cgi/doi/10.1073/pnas.0307513100
Chen
OCR for page 127
BREZIN E 1990
V236 P1"
DAVID F 1988 DINE M 1985 DIXON L 1986
V3 P1651 V156 P55 W74 P285
GROSS DJ 1985 —Am, \ ~
V256 P253 ~ ~ ~ \~6
DISTLER J 1989
~~J
A,,:.
, ~
~ ~~.~-
FRIEDAN D 1986
V271 P93 ~3
CANDELAS P 1985 ~ j
V258 P46 ~ --------
GREEN MB, SCH~
JH 19" V149 P117 \ ~ o
~ e 0 ·
POLCHINSKi J 1~5 .
V75 P4724
fit
BERENSTEIN D 2003
BOOK _
GALLAN CG 1985
f26Z P593
> - ~ Aft
. ~
< -
-
f'
; 4,^ >
~P~
~~ ~T1~112
- Tl-~llZ ~ Tuming One
,{~` ·~ ~ Pivot points
· 4985-1987 ~ 1988-1990 · 1991-1993 · 19~1996 ~ 1997-1999 ~ 2000-2002 ~ 2003
Fig. 4. A 624-node merged network with global pruning by using Pathfinder (q = N- 1, r = =). See a color version at www.pages.drexel.edu/~cc345/
citespace/Figure4.png.
ence Citation Report (1981-1998), Candelas et al. 's article has
a total of 1,538 citations during that period, and its average
annual citation is 110. The 1995 Polchinski article can be easily
found at the lower center of the map; according to Schwarz, it
explained the concept of D-branes, a crucial ingredient in almost
all modern string theory research. It appears to be more of a hub
than a pivot node, connected by links of only two colors, brown
(1997-1999) and yellow (2000-2002~. To the left of Polchinski's
article is a cluster centered by the 1998 Maldacena article.
Schwarz noted that in this article, Maldacena made a major new
discovery that in certain circumstances relates string theories to
quantum field theories.
The comments from domain experts have confirmed that both
versions of the merged network indeed highlight significant
articles, and these articles tend to have unique topological
properties that distinguish them from other articles. The globally
pruned version is easier to explore than the local-pruning-only
version.
Discussion
The results are particularly encouraging because the presence of
pivot nodes enables us to narrow down the visual search quickly
to a small number of good candidate nodes for intellectual
turning points. An easy identification of such turning points is an
important and necessary step toward effective detection of
paradigmatic changes in a knowledge domain. The small net-
work is particularly clear, containing both turning points. The
larger network without pruning is cluttered, although it is still
possible to identify several pivot points.
Chen
The interpretation and validation of the visualizations have
greatly benefited from help from leading scientists in the knowl-
edge domain. The work has also shown that using a variable
threshold could be a potentially good practice for citation
analysis in general.
In comparison to our earlier visualization of the superstring
cocitation network (8), this method tends to produce more
distinct visual features for key articles. More importantly, such
visual features appear to be independent to the amount of
citations of a node. In other words, a lower citation rate is not
necessarily preventing a node from having salient visual features,
suggesting that cocitations must have played a greater role. Pivot
nodes can be identified even if they have relatively fewer
citations. This could be a particularly useful feature for the
detection of significant articles that could be easily overlooked
by falling below a single high-citation threshold.
The 1984 Green-Schwarz article for the first revolution is a
typical pivot node, whereas the 1995 Polchinski article for the
second revolution is more of a hub than a pivot node. This
finding suggests that before we have further evidence, it would
be sensible to examine both types of visualizations, pruned and
unpruned, in a study of intellectual turning points.
In comparison to other methods for detecting changes of
networks over time, our approach simplifies cognitively demand-
ing tasks of comparing a sequence of network snapshots. The
progressive visualization method allows us to focus on much
simpler tasks of locating pivot nodes and cluster centers. The
color-coded links enables the user to trace temporal patterns
through the network visualization.
PNAS 1 April 6, 2004 1 vol. 101 1 suppl. ~ 1 5309
OCR for page 128
The progressive visualization method introduced here has
practical implications. It provides scientists with a roadmap of
their own field. Witten commented, "It was fun to look at it." A
longstanding challenge is to be able to visualize cocitation
networks of a domain as quickly as new bibliographic data
become available so that one can monitor the changes of a
domain more closely on a monthly or even weekly basis. The
approach provides a practical starting point. Users have the
flexibility to slice a time interval into smaller as well as larger
segments.
Using overlapped time slices could be a valuable alternative to
explore in future studies. Currently, adjacent time slices are
mutually exclusive to highlight the magnitude of a potentially
important change, whereas overlapping slice segments may blur
such changes and make them less obvious to detect.
An unsolved issue is concerned with the detection of abrupt
changes in citations within a short period. We normalized the
citations of an article by its publication age. Additional metrics
of pivot nodes should augment the power of visual inspection
even further. Knowledge discovery and data-mining techniques,
such as Kleinberg's burst-detection technique (35), are expected
to play a substantial role in identifying a paradigm shift.
Finally, the role of domain experts in KDViz needs to be
further investigated. Experts in the fields are the best sources to
seek validations and interpretations. On the other hand, one
should also use domain visualizations with caution; and it should
be made clear that algorithmically generated domain visualiza-
tions, however crafted, merely portray the complexity of an
underlying domain from a limited perspective. If KDViz can
stimulate scientists to look at their own field from a different
perspective and pose new questions about the evolution of their
domain, KDViz will ultimately become a practical tool to study
science itself.
1. Chen, C. (2003) Mapping Scientific Frontiers: The Quest for Knowledge Visual-
ization (Springer, London).
2. Barabasi, A.-L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A. & Vicsek, T.
(2002) Phys. A 311, 590-614.
3. Newman, M. E. J. (2001) Proc. Natl. Acad. Sci. USA 98, 404-409.
4. Garfield, E. & Small, H. (1989) inInnovation:At the Crossroads Between Science
and Technology, eds. Kranzberg, M., Elkana, Y. & Tadmor, Z. (Neaman,
Haifa), pp. 51-65.
5. Small, H. & Greenlee, E. (1989) Commun. Res. 16, 642-666.
6. Kuhn, T. S. (1962) The Structure of Scientific Revolutions (Univ. of Chicago
Press, Chicago).
7. Small, H. G. (1977) Soc. Stud. Sci. 7,139-166.
8. Chen, C. & Kuljis, J. (2003) J Am. Soc. Inf: Sci. Technol. 54, 435-446.
9. Price, D. D. (1965) Science 149, 510-515.
10. Albert, R. & Barabasi, A. (2002) Rev. Mod. Phys. 74, 47-97.
11. Dorogovtsev, S. N., Mendes, J. F. F. & Samukhin, A. N. (2000) Phys. Rev. Lett.
85, 4633-4636.
12. Newman, M. (2001) Phys. Rev. E 64.
13. Havre, S., Hetzler, E., Whitney, P. & Nowell, L. (2002) IEEE T. Vis. Comput.
Graphics 8, 9-20.
14. Carroll, J. D. & Chang, J.-J. (1970) Psychometrika 35, 283-319.
15. Gower, J. C. (1975) Psychometrika 40, 33-51.
16. Bookstein, F. L. (1989) IEEE T. Pattern Anal. 11, 567-585.
17. Brandes, U. & Corman, S. R. (2003) Ini Visual. 2, 40-50.
18. Erten, C., Harding, P. J., Kobourov, S. G., Wampler, K. & Yee, G. (2003)
Technical Report TR0304 (Univ. of Arizona, Tucson, AZ).
5310 1 www.pnas.org/cgi/doi/10.1073/pnas.0307513100
Conclusion
The progressive KDViz method simplifies the tasks of tracking
significant changes of a knowledge domain's cocitation network
over time. Cognitively demanding tasks of comparing complex
networks back and forth are simplified to tasks of locating pivot
points and cluster centers in visualized networks.
The divide-and-conquer strategy maximizes the strengths of
algorithms and reduces the influence of their weaknesses. The
cosine cocitation coefficients are effective enough to pick up the
most intellectually significant articles, whereas the Pathfinder-
enhanced version improved the quality even further.
CITESPACE provides an experiment platform to investigate new
ideas and compare existing approaches. We plan to make a
further refined version of CITESPACE available in the near future
to researchers, practitioners, and educators in various disciplines
and obtain their first-hand experience in capturing the changes
of their own domains.
Further studies and in-depth case studies of progressive
KDViz should be encouraged. For example, can this method
detect the merge of two domains or the split of a single domain
into a few new ones? Can this method detect scientific revolu-
tions in other disciplines? Will it work with alternative repre-
sentations of a knowledge domain, such as the preprint archives
used by physicists and other sources? KDViz is a challenging
route, but it is also potentially rewarding for scientists in so many
different knowledge domains to have easy access to the big
picture of their own fields.
We give special thanks to John Schwarz (California Institute of Tech-
nology) and Edward Witten (Princeton University) for help in inter-
preting the visualizations. The 2002 Institute for Scientific Information/
American Society for Information Science and Technology Citation
Analysis Research award is acknowledged.
19. Misue, K., Eades, P., Lai, W. & Sugiyama, K. (1995) J. Visual Lang. Comput.
6, 183-210.
20. North, S. C. (1995) in Proceedings of Graph Drawing (GD'95), ed. Brandenburg,
F. J. (Springer, New York), pp. 409-418.
21. Batagelj, V. & Mrvar, A. (1998) Connections 21, 47-57.
22.
23.
24.
25.
26.
Ware, C., Purchase, H., Colpoys, L. & McGill, M. (2003) Inf. Visual. 1, 103-110.
Schvaneveldt, R. W. (1990) PathfinderAssociative Networks (Ablex, Norwood, NJ).
Chen, C. (1999) Inform. Process. Manag. 35, 401-420.
Chen, C. & Paul, R. J. (2001) Computer 34, 65-71.
Chen, C., Cribbin, T., Macredie, R. & Morar, S. (2002) J Am. Soc. Inf. Sci.
Technol. 53, 678-689.
27. Chen, C., Kuljis, J. & Paul, R. J. (2001) IEEE T. Syst. Man. Cy. C 31, 518-529.
28. White, H. (2003) J Am. Soc. Inf. Sci. Technol. 54, 423-434.
29. van Raan, A. (2000) Scientometrics 47, 347-362.
30. Ahlgren, P., Jarneving, B. & Rousseau, R. (2003) JAm. Soc. Inf. Sci. Technol.
54, 550-560.
31. White, H. D. (2003) J Am. Soc. Inf. Sci. Technol. 54, 1250-1259.
32. Chen, C. & Morris, S. (2003) in IEEE Symposium on Information Visualization
(InfoVis'03) (IEEE Computer Society Press, Seattle), pp. 67-74.
33. Kamada, T. & Kawai, S. (1989) Inform. Process. Lett. 31, 7-15.
34. Schwarz, J. H. (1996) arXiv:hep-th/9607067 (http://arxiv.org/PS cache/hep-
th/pdf/9607 /9607067.pdf).
35. Kleinberg, J. (2002) in Proceedings of the 8th ACM SICKDD International
Conference on Knowledge Discovery and Data Mining (ACM, New York), pp.
91-101.
Chen
OCR for page 129
Arthur M. Sack/er
COLLOQ U IA
OF THE NATIONAL ACADEMY OF SCIENCES
At'
Mapping Knowledge Domains
May 9-1 1, 2003
Arnold and Mabel Beckman Center, Irvine, CA
Organized by Richard M. Shiffrin and Katy Borner
Program
Friday, May 9
Keynote Address
Eugene Garfield, ISI
A Historiograph of Mapping Knowledge Domains
Session I: Data Bases, Data Format, and Access
Monika Henzinger, Google, and Steve Lawrence, NEC Research Institute
Extracting Knowledge from the World Wide Web
Paul Ginsparg, Cornell University
Mapping Subsets of Scholarly Information
Saturclay, May 10
Session Il: Data Analysis Algorithms
Thomas K. Landauer, University of Colorado at Boulder and Knowledge Analysis
.
ec 1no ogles
From Paragraph to Graph
M. E. J. Newman, University of Michigan
The Structure of Scientific Collaboration Networks
Elena Erosheva, University of Washington
Using Mixed Membership Models for Mapping Knowledge Domains
Thomas L. Griffiths, Stanford University, and Mark Steyvers, University of
California, Irvine
Topic Dynamics in Knowledge Domains
Jon Kleinberg, Cornell University
Enhancing Web Sites with Usage Data
Kate McCain, Drexel University
Combining Bibliometric and Knowledge Elicitation Techniques to Map a
Knowledge Domain
OCR for page 130
Sunday, May 11
Session tIl: Visualization and Interaction Design
Colin Ware, University of New Hampshire
Information Seeking and the Objects of Visual Attention
Alan M. MacEachren, Pennsylvania State University
Geovisualization for Constructing and Sharing Concepts
Chaomei Chen, Drexel University
Paradigms, Debates, and Puzzles in Science: A Visual Exploration
Katy Borner, Indiana University
The Simultaneous Evolution of Article and Author Networks in PNAS
Session tV: Promising Applications
Susan Dumais, Microsoft Research
Visualizing Search Results
Beth Hetzler, Pacific Northwest National Laboratory
Analysis Experiences Using Information Visualization
Kevin W. Boyack, Sandia National Laboratories
An Indicator-Based Characterization of the Proceedings of the National Academy
of Sciences
Francis Narin, CHI Research, Inc.
From Science Papers to Technology Patents and on to Company Financial Performance
OCR for page 131
OCR for page 132
THE NATIONAL ACADEMIES
Advisers ~ the Nation on Science, Engineering, and Medicine
The National Academy of Sciences is a private, nonprofit, self-perpetuating
society of distinguished scholars engaged in scientific and engineering research,
dedicated to the furtherance of science and technology and to their use for the
general welfare. Upon the authority of the charter granted to it by the Congress in
1863, the Academy has a mandate that requires it to advise the federal government
on scientific and technical matters. Dr. Bruce M. Alberts is president of the
National Academy of Sciences.
The National Academy of Engineering was established in 1964, under the
charter of the National Academy of Sciences, as a parallel organization of outstand-
ing engineers. It is autonomous in its administration and in the selection of its
members, sharing with the National Academy of Sciences the responsibility for
advising the federal government. The National Academy of Engineering also
sponsors engineering programs aimed at meeting national needs, encourages
education and research, and recognizes the superior achievements of engineers.
Dr. Wm. A. Wulf is president of the National Academy of Engineering.
The Institute of Medicine was established in 1970 by the National Academy of
Sciences to secure the services of eminent members of appropriate professions in
the examination of policy matters pertaining to the health of the public. The
Institute acts under the responsibility given to the National Academy of Sciences
by its congressional charter to be an adviser to the federal government and, upon
its own initiative, to identify issues of medical care, research, and education.
Dr. Harvey ~ Fineberg is president of the Institute of Medicine.
The National Research Council was organized by the National Academy of
Sciences in 1916 to associate the broad community of science and technology
with the Academy's purposes of furthering knowledge and advising the federal
government. Functioning in accordance with general policies determined by the
Academy, the Council has become the principal operating agency of both the
National Academy of Sciences and the National Academy of Engineering in
providing services to the government, the public, and the scientific and engineering
communities. The Council is administered jointly by both Academies and the
Institute of Medicine. Dr. Bruce M. Alberts and Dr. Wm. A. Wulf are chair
and vice chair, respectively, of the National Research Council.
www.national-academies.org
The articles in the Proceedings of the National Academy of Sciences
report original research by independent authors and do not necessarily
represent the views of the National Academies.
Representative terms from entire chapter:
knowledge domain
