Papers from PNAS of the USA 101 (Suppl) April 6, 2004

Mon Aug 2 17:23:14 EDT 2004

FULL TEXT AVAILABLE AT : http://www.pnas.org/cgi/content/full/101/suppl_1/5186

Monika Henzinger:   E-mail Address: monika at google.com

Author(s): Henzinger, M; Lawrence, S

Title: Extracting knowledge from the World Wide Web

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5186-5191 Suppl. 1 APR 6 2004
Language: English
Document Type: Article

Abstract: The World Wide Web provides a unprecedented opportunity to
automatically analyze a large sample of interests and activity in the world.
We discuss methods for extracting knowledge from the web by randomly
sampling and analyzing hosts and pages, and by analyzing the link structure
of the web and how links accumulate over time. A variety of interesting and
valuable information can be extracted, such as the distribution of web pages
over domains, the distribution of interest in different areas, communities
related to different topics, the nature of competition in different
categories of sites, and the degree of communication between different
communities or countries.

Addresses: Google Inc, Mountain View, CA 94043 USA

Reprint Address: Henzinger, M, Google Inc, 2400 Bayshore Pkwy, Mountain
View, CA 94043 USA.

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Times Cited: 1
Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 6
ISI Document Delivery No.: 812EO

_______________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5192
E-mail Address: kboyack at sandia.gov

Author(s): Boyack, KW

Title: Mapping knowledge domains: Characterizing PNAS

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5192-5199 Suppl. 1 APR 6 2004

Language: English
Document Type: Article

Abstract: A review of data mining and analysis techniques that can be used
for the mapping of knowledge domains is given. Literature mapping techniques
can be based on authors, documents, journals, words, and/or indicators. Most
mapping questions are related to research assessment or to the structure and
dynamics of disciplines or networks. Several mapping techniques are
demonstrated on a data set comprising 20 years of papers published in PNAS.
Data from a variety of sources are merged to provide unique indicators of
the domain bounded by PNAS. By using funding source information and citation
counts, it is shown that, on an aggregate basis, papers funded jointly by
the U.S. Public Health Service (which includes the National Institutes of
Health) and non-U.S. government sources outperform papers funded by other
sources, including by the U.S. Public Health Service alone. Grant data from
the National Institute on Aging show that, on average, papers from large
grants are cited more than those from small grants, with performance
increasing with grant amount. A map of the highest performing papers over
the 20-year period was generated by using citation analysis. Changes and
trends in the subjects of highest impact within the PNAS domain are
described. Interactions between topics over the most recent 5-year period
are also detailed.
Addresses: Sandia Natl Labs, Computat Comp Informat & Math Ctr, Albuquerque,
NM 87185 USA

Reprint Address: Boyack, KW, Sandia Natl Labs, Computat Comp Informat & Math
Ctr, POB 5800, Albuquerque, NM 87185 USA.

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Times Cited: 1
Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 8
ISI Document Delivery No.: 812EO

___________________________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5200

E-mail Address: mejn at umich.edu

Author(s): Newman, MEJ

Title: Coauthorship networks and patterns of scientific collaboration

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5200-5205 Suppl. 1 APR 6 2004
Language: English
Document Type: Article

Abstract: By using data from three bibliographic databases in biology,
physics, and mathematics, respectively, networks are constructed in which
the nodes are scientists, and two scientists are connected if they have
coauthored a paper. We use these networks to answer a broad variety of
questions about collaboration patterns, such as the numbers of papers
authors write, how many people they write them with, what the typical
distance between scientists is through the network, and how patterns of
collaboration vary between subjects and over time. We also summarize a
number of recent results by other authors on coauthorship patterns.
Addresses: Univ Michigan, Ctr Study Complex Syst, Ann Arbor, MI 48109 USA;
Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA
Reprint Address: Newman, MEJ, Univ Michigan, Ctr Study Complex Syst, Ann
Arbor, MI 48109 USA.

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Times Cited: 1

Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424

Source Item Page Count: 6

ISI Document Delivery No.: 812EO

_______________________________________________

full text available at :
http://www.pnas.org/cgi/content/full/101/suppl_1/5249

E-mail Address: selman at cs.cornell.edu

Author(s): Hopcroft, J; Khan, O; Kulis, B; Selman, B

Title: Tracking evolving communities in large linked networks

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5249-5253 Suppl. 1 APR 6 2004

Language: English Document Type: Article

Abstract: We are interested in tracking changes in large-scale data by
periodically creating an agglomerative clustering and examining the
evolution of clusters (communities) over time. We examine a large real-world
data set: the NEC CiteSeer database, a linked network of >250,000 papers.
Tracking changes over time requires a clustering algorithm that produces
clusters stable under small perturbations of the input data. However, small
perturbations of the CiteSeer data lead to significant changes to most of
the clusters. One reason for this is that the order in which papers within
communities are combined is somewhat arbitrary. However, certain subsets of
papers, called natural communities, correspond to real structure in the
CiteSeer database and thus appear in any clustering. By identifying the
subset of clusters that remain stable under multiple clustering runs, we get
the set of natural communities that we can track over time. We demonstrate
that such natural communities allow us to identify emerging communities and
track temporal changes in the underlying structure of our network data.
Addresses: Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA; Google Inc,
Mountain View, CA 94043 USA; Univ Texas, Dept Comp Sci, Austin, TX 78712 USA
Reprint Address: Selman, B, Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA.

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Times Cited: 1
Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 5
ISI Document Delivery No.: 812EO

_______________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5261

E-mail Address: fil at indiana.edu
Author(s): Menczer, F

Title: Evolution of document networks

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5261-5265 Suppl. 1 APR 6 2004

Language: English Document Type: Article

Abstract: How does a network of documents grow without centralized control?
This question is becoming crucial as we try to explain the emergent
scale-free topology of the World Wide Web and use link analysis to identify
important information resources. Existing models of growing information
networks have focused on the structure of links but neglected the content of
nodes. Here I show that the current models fail to reproduce a critical
characteristic of information networks, namely the distribution of textual
similarity among linked documents. I propose a more realistic model that
generates links by using both popularity and content. This model yields
remarkably accurate predictions of both degree and similarity distributions
in networks of web pages and scientific literature.
Addresses: Indiana Univ, Sch Informat, Bloomington, IN 47408 USA
Reprint Address: Menczer, F, Indiana Univ, Sch Informat, Bloomington, IN
47408 USA.

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Times Cited: 2
Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 5
ISI Document Delivery No.: 812EO

__________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5266

E-mail Address: katy at indiana.edu

Author(s): Borner, K; Maru, JT; Goldstone, RL

Title: The simultaneous evolution of author and paper networks

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5266-5273 Suppl. 1 APR 6 2004

Language: English  Document Type: Article

Abstract: There has been a long history of research into the structure and
evolution of mankind's scientific endeavor. However, recent progress in
applying the tools of science to understand science itself has been
unprecedented because only recently has there been access to high-volume and
high-quality data sets of scientific output (e.g., publications, patents,
grants) and computers and algorithms capable of handling this enormous
stream of data. This article reviews major work on models that aim to
capture and recreate the structure and dynamics of scientific evolution. We
then introduce a general process model that simultaneously grows coauthor
and paper citation networks. The statistical and dynamic properties of the
networks generated by this model are validated against a 20-year data set of
articles published in PNAS. Systematic deviations from a power law
distribution of citations to papers are well fit by a model that
incorporates a partitioning of authors and papers into topics, a bias for
authors to cite recent papers, and a tendency for authors to cite papers
cited by papers that they have read. In this TARL model (for topics, aging,
and recursive linking), the number of topics is linearly related to the
clustering coefficient of the simulated paper citation network.

Addresses: Indiana Univ, Sch Lib & Informat Sci, Bloomington, IN 47405 USA;
Indiana Univ, Dept Comp Sci, Bloomington, IN 47405 USA; Indiana Univ, Dept
Psychol, Bloomington, IN 47405 USA

Reprint Address: Borner, K, Indiana Univ, Sch Lib & Informat Sci,
Bloomington, IN 47405 USA.

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Times Cited: 2

Publisher: NATL ACAD SCIENCES

Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 8
ISI Document Delivery No.: 812EO

________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5291

E-mail Address: samorri at okstate.edu

Author(s): Morris, SA; Yen, GG

Title: Crossmaps: Visualization of overlapping relationships in collections
of journal papers

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5291-5296 Suppl. 1 APR 6 2004

Language: English   Document Type: Article

Abstract: A crossmapping technique is introduced for visualizing multiple
and overlapping relations among entity types in collections of journal
articles. Groups of entities from two entity types are crossplotted to show
correspondence of relations. For example, author collaboration groups are
plotted on the x axis against groups of papers(research fronts)on the y
axis. At the intersection of each pair of author group/research front pairs
a circular symbol is plotted whose size is proportional to the number of
times that authors in the group appear as authors in papers in the research
front. Entity groups are found by agglomerative hierarchical clustering
using conventional similarity measures. Crossmaps comprise a simple
technique that is particularly suited to showing overlap in relations among
entity groups. Particularly useful crossmaps are: research fronts against
base reference clusters, research fronts against author collaboration
groups, and research fronts against term co-occurrence clusters. When
exploring the knowledge domain of a collection of journal papers, it is
useful to have several crossmaps of different entity pairs, complemented by
research front timelines and base reference cluster timelines.

Addresses: Oklahoma State Univ, Stillwater, OK 74078 USA

Reprint Address: Morris, SA, Oklahoma State Univ, 202 Engn S, Stillwater, OK
74078 USA.

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Times Cited: 0

Publisher: NATL ACAD SCIENCES

Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA

ISSN: 0027-8424
Source Item Page Count: 6
ISI Document Delivery No.: 812EO

______________________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5297

E-mail Address: whitehd at drexel.edu

Author(s): White, HD; Lin, X; Buzydlowski, JW; Chen, CM

Title: User-controlled mapping of significant literatures

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5297-5302 Suppl. 1 APR 6 2004

Language: English Document Type: Article

Abstract: We apply a version of our web-based literature-mapping system to
PNAS for 1971-2002, as indexed by the National Library of Medicine and the
Institute for Scientific Information. Given a single input term from a user,
a medical subject heading, a cocited author, or a cocited journal, PNASLINK
rapidly displays views in which that term and the other 24 terms that most
frequently co-occur with it in a bibliographic database are interrelated in
ways suggesting fruitful combinations for document retrieval. The
interrelationships are produced by two algorithms, pathfinder networks and
Kohonen-style self-organizing maps. PNASLINK displays are themselves
interactive interfaces that can retrieve documents from digital libraries
(e.g., PNAS Online). This style of visualizing knowledge domains is called
"localized" because it does not attempt to map the indexing of literatures
in full but concentrates on the top terms in an "associative thesaurus"
reflecting user interests. It also permits swift remappings, as the user
recognizes terms worth pursuing. PNASLINK is illustrated with maps drawn
from the literature of population genetics. Some comparative and evaluative
comments are added, one from a domain expert indicating that the face
validity of the system may be tempered by insufficient specificity in the
indexing terms being mapped.

Addresses: Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA

Reprint Address: White, HD, Drexel Univ, Coll Informat Sci & Technol,
Philadelphia, PA 19104 USA.

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Times Cited: 0
Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 6
ISI Document Delivery No.: 812EO

_______________________________________

FULL TEXT AVAILABLE AT :
http://www.pnas.org/cgi/content/full/101/suppl_1/5303

E-mail Address: chaomei.chen at cis.drexel.edu

Author(s): Chen, CM

Title: Searching for intellectual turning points: Progressive knowledge
domain visualization

Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES
OF AMERICA, 101: 5303-5310 Suppl. 1 APR 6 2004

anguage: English Document Type: Article

Abstract: This article introduces a previously undescribed method
progressively visualizing the evolution of a knowledge domain's cocitation
network. The method first derives a sequence of cocitation networks from a
series of equal-length time interval slices. These time-registered networks
are merged and visualized in a panoramic view in such away that
intellectually significant articles can be identified based on their
visually salient features. The method is applied to a cocitation 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 panoramic 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.
Addresses: Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA

Reprint Address: Chen, CM, Drexel Univ, Coll Informat Sci & Technol, 3141
Chestnut St, Philadelphia, PA 19104 USA.

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Times Cited: 1
Publisher: NATL ACAD SCIENCES
Publisher Address: 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
ISSN: 0027-8424
Source Item Page Count: 8
ISI Document Delivery No.: 812EO