In the pharmaceuticals industry, locating key opinion leaders (KOLs) is essential — and very difficult — for clinical trials and other assessments. We provide a method and an in-depth example, of using social network analysis (SNA) to identify these critical players.

1. Identifying Key Opinion Leaders Using
Social Network Analysis
By applying a more scientific approach to finding and engaging opinion
leaders, pharma companies can more effectively scale and accelerate
their R&D efforts, thereby gaining an edge over their rivals.
Executive Summary
Key opinion leaders (KOLs) undeniably play a
very important role in the life sciences industry.
Pharma companies typically engage them during
the later stages of clinical trials and during drug
promotions. However, as regulatory approval
challenges mount, many drug manufacturers are
seeking deeper collaboration with opinion leaders
throughout the drug development process.
Identifying KOLs requires broad and up-to-date
experience with these individuals, something
pharma companies lack. In fact, most life science
companies delegate this exercise to third parties.
In turn, these providers make use of “tried-and-
true” methods of engaging KOLs, including
literature database search and survey methods.
In an age where consumers hold sway over
purchase considerations and channel preferences
across industries, these traditional approaches
are increasingly ineffective and exacerbate the
risk of “experience bias.”
This white paper offers a different approach to
identifying key opinion leaders — social network
analysis (SNA). SNA is based on social networks
that are derived from interactions between indi-
viduals and thereby model meaningful social
relationships. Besides highlighting the benefits of
SNA over traditional methods, this paper demon-
strates a simple application of SNA on a co-author
or author-collaboration network for authors
of scientific publications in the field of medical
genetics. The focus of this demonstration is to
identify thought leaders who could be sought for
advice and research collaboration. Many of the
authors shortlisted as opinion leaders (based on
our analysis) have made significant contributions
in the field of medical genetics. Besides having
led key research projects, many of the short-
listed authors are members of advisory boards
and have held leadership or executive positions
in important organizations such as the National
Society of Genetic Counselors and the European
Board of Medical Genetics.
Why Key Opinion Leaders Are Critical
to Life Sciences Companies
KOLs, commonly referred to as thought leaders,
play a very important role in the life sciences
industry, especially in the adoption and usage of
new products. KOLs are typically healthcare pro-
fessionals (HCPs) who hold senior positions in the
medical community. However, they may also be
members of the academic community who have
contributed significantly to or have advanced
knowledge in specific therapeutic areas. By virtue
of their position or expertise, KOLs tend to have
an asymmetric influence on physicians, and hence
on prescribing behavior and treatment guidelines.
cognizant 20-20 insights | june 2015
• Cognizant 20-20 Insights

2. Although involvement of KOLs in drug promotion
is a contentious issue,1
they play a vital role in
other phases of drug development as well (see
Figure 1). KOLs collaborate with companies on
Phase III trials, influence basic research, conduct
Phase IV outcomes research and assist in the
identification of new research areas or product
lines. They may also be involved in a drug’s
regulatory review process and provide informa-
tion on comparative benefits and side effects of a
drug. Post approval (Phase IV), most major phar-
maceuticals companies deploy teams of medical
science liaisons (MSLs) to respond to medical
inquiries from KOLs in specific therapeutic areas.
Identifying Key Opinion Leaders
KOL management, which includes segmenting
opinion leaders and managing relationships with
them, has well-structured practices. However,
KOL identification, which is the precursor to KOL
management, provides a lot of scope for improve-
ment. Most pharmaceuticals companies depend
on third parties to provide KOL databases that,
in turn, make use of traditional methods like
literature searches and survey methods. These
methods have their own characteristic limitations.
Figure 2 lists some of the commonly followed tra-
ditional methods and their limitations.
Engaging with KOLs on Drug Development
Traditional KOL Methods
Typical KOL Activities in Drug Development Process
Research &
Drug Discovery
 Identify research areas.
 Provide insights on disease areas.
 Undertake research on drug
company’s behalf.
Approval
Phase IV
(Post-approval)
Clinical Trials Phase II
and Phase III
 Consult on clinical
program design.
 Provide advice on
product profile.
 Advice on regulatory/
approval process.
 Provide information on
drug efficacy or benefits.
 Give presentations
at congresses.
 Publish articles or papers.
 Build product awareness.
Figure 1
Figure 2
Observation Method
An outsider, who is familiar with
a subject area or community,
identifies who are the most influ-
ential members in that community.
Limitations
• Observer may be more biased
towards well known or high
profile members.
• Observer has limited ability to
track all interactions. Hence,
such methods are well-suited
only to small communities for
identifying regional opinion
leaders.
Survey Methods
Individuals working in a given subject area are
asked to provide names of others who they rely
on for advice or who they think are the thought
leaders in the given subject area.
Alternatively, individuals may be asked to rate
their peers in terms of various criteria like quality
of education, degree of contribution to the
subject area or quality of contributions.
Limitations
• Expense increases as sample size increases.
• Suffers from sampling bias — i.e., the responses
and outcomes of the survey are highly
dependent on the chosen sample. Surveyors
need to ensure that the sample size is suf-
ficiently large and the members in the sample
are reasonably representative of the intended
population.
• Results may also get skewed due to non-
responses. Both methods also suffer from
volunteer response bias.
Literature Searches
Companies employ researcher(s) to search through
various publication databases and other similar
sources and identify and profile prominent thought
leaders in a research area. The researcher typically
uses search engines and other tools in order to
perform an exhaustive search and go through huge
volumes of data.
Limitations
• Time-consuming as a lot of the search and
analysis work is manual.
• Results depend on the search parameters as well
as the researcher’s knowledge of the research
area. The criteria used for identifying opinion
leaders do not take into account social informa-
tion. For example, number of publications or
number of conferences attended are often used as
metrics. However, such metrics cannot account for
relevant information like whether the publication
was well acknowledged by peers or whether the
author made any significant contribution to the
conference.
• Scalability is constrained by the limited ability of
researcher to search for data.

3. Social networks are derived from social interactions like
relationships, collaboration, etc. and therefore model
social prestige. It’s a natural tendency in a social group to
get associated to respected members of the groups, and
hence respected members of a community will tend to be
connected to the majority of the community members either
directly or indirectly via others. Thus, social networks could
provide valuable insights on thought leaders.
People generally prefer to work with others who share
similar interests and values. Structural analysis of a network
could provide additional insights like the presence of hidden
cohesive groups or clusters.
Scalable: SNA can be applied to large networks, and thus
can accommodate an entire community rather than just a
sample of members.
SNA is free from responder bias as well as other bias
associated with survey and observation methods.
Why Social Network Analysis
Using SNA for KOL Identification
The limitations of the traditional methods have
pushed the industry toward innovative methods
such as SNA to help identify opinion leaders.
In SNA, the interactions between participating
actors are tracked as relationships or connections
and are used to construct a social network. These
relationships, which have developed over time,
form the basis of collaboration and information
sharing within and across communities and thus
provide a collective value referred to, by Putnam,
as social capital.2
SNA leverages this information
by analyzing the social structure of a network and
the position of actors within a network.
Consider, for example, a research community
collaborating on a specific therapeutic area.
This could typically involve a large community
of researchers ranging in the thousands and
spanning multiple geographies. The network
could include various types of actors including
pharmaceuticals companies, educational institu-
tions, advisory boards and regulatory bodies. In
this case, a social network could be created to
capture community members collaborating on
research.
Analyzing this network could then provide rich
contextualinformation.Forexample,analyzingthe
position of members in the network could provide
insights into how important they are within the
research community. Structural analysis of the
network could also provide additional insights
such as the presence of hidden cohesive groups.
Such insights are generally not possible with tra-
ditional methods. A research report found that
bibliometric, or literature-based, methods fail to
identify over 50% of thought leaders compared
to SNA.3
Another key tenet of SNA is that it is scalable, and
thus can be used on an entire community rather
than on just a sample of members. Moreover, SNA
is typically free from volunteer response bias as
well as other bias associated with survey and
observation methods (see sidebar below).
Understanding SNA
To apply SNA, organizations need to first
understand what they are getting into. Below are
the definitions we use:
• Social network: A social network is a special
type of graph that depicts relationships
between actors. The actors of interest (individ-
uals, groups, companies, etc.) are the nodes and
relationships between the actors are the arcs
or ties. In 1953, Moreno first introduced the idea
3cognizant 20-20 insights

4. 4cognizant 20-20 insights
Figure 3
A Typical Social Network Analysis Methodology
Derive
Insights
Profile
Information
Literature
Databases
Other
Sources
Data Cleansing
and Data
Conversion
Concept
Extraction &
Network
Creation
Data
Visualization &
Network
Measures
to represent social interactions in a network
structure, called a sociogram.4
Facebook is a
classic example of a social network where the
nodes represent the Facebook users or groups
and friendships are represented as ties.
• Social network analysis: As explained by
McGuire, this “is the process of applying
analysis techniques to a social network to
answer specific questions about that network.
Often, these questions focus around who
key actors are in the social network. Other
questions may be looking for groups of actors
with strong ties to one another or how best to
improve the communications or productivity of
the group being analyzed.”5
Applying SNA: General Methodology
A typical methodology for applying SNA is shown
in Figure 3. Based on the end purpose that the
analysis intends to serve, data is collected from
various sources; it is then cleansed and processed
to remove duplicates and converted into a usable
format.
Concept extraction: This refers to the process
of extracting the nodes and ties from the data.
Selection of nodes depends on the actors on
which the analysis needs to focus and ties are
chosen based on the nature of interaction that
needs to be represented. Complex networks may
include attributes or properties for nodes or ties
(e.g., the age or qualification of each person or
node or strength of a tie based on frequency of
interaction between the participating nodes).
Once the relevant data has been extracted, a
network is created.
Applying network measures: The objective of
the analysis, as well as the nature of the network,
will determine the specific measures that need to
be applied to derive insights from the network.
This can vary from applying simple centrality
measures to complex measures that analyze how
the network is structured or how it evolves with
time. (For a detailed explanation on centrality
measures, refer to the Appendix on page 10.)
Identifying KOLs from a Coauthor’s
Network Using SNA
This section explains the use of the centrality
measures on a coauthors or author-collabora-
tion network to identify key profiles. Data was
collected for all publications in the field of medical
genetics6
during the last ten years (2004 – 2014)
from PubMed.7
From this data, a network based on
“author-collaboration” was created. The network
represents a social network of “who has collabo-
rated with whom” (i.e., the nodes or vertices are
the authors and a tie or link between a pair of
nodes implies that they have collaborated on at
least one publication).
The focus of our analysis is to identify thought
leaders for the purpose of advocacy or research
collaboration rather than for drug promotion;
hence, our decision to use scientific publica-
tions (in the chosen field) from PubMed. For the
purpose of drug promotion, other factors such
as number of publications, willingness to deliver
seminars or media presence of the individual
may be taken into account. As stated above, the
purpose of the analysis will inform the data to be
collected and the measures to be applied.

5. cognizant 20-20 insights 5
The author-collaboration network was created
using “igraph” library8
for R.9
For each publica-
tion, the authors have been extracted and each
coauthor combination pair is represented as a
tie. Publications that have only one author were
ignored. Duplicate author names have been
replaced manually.
Some articles in the data set have a large set
of coauthors, which tends to produce a bias in
centrality measures, especially ”degree” (an
article with a large number of coauthors will
result in a high degree value for each of the
coauthors). To remove such bias, we may consider
only those authors who make significant con-
tributions to an article or publication. However,
as such information is rarely available, we have
assumed that only a certain number of authors
mentioned in a publication make significant con-
tributions. A distribution of authors per publi-
cation for our data is shown in Figure 4. In our
research, 98% of publications or articles have 12
or fewer authors. For our analysis, we considered
only the first 12 coauthors of any publication as
significant coauthors and used them for network
construction.
The overall network comprises 6,151 authors and
20,908 ties among them. This network is not
completely connected and has multiple isolated
groups of authors (i.e., components). The “igraph”
library in R (which has been used in our analysis
to calculate centrality) makes an approximation
when calculating closeness values of nodes that
are not connected to one or more nodes. For the
sake of simplicity, we focus our analysis only on
the largest connected component of the network
which has 2,517 authors and 10,375 ties (see
Figure 5). Note that this component accounts for
about 41% of nodes and 50% of ties of the overall
network. The centrality measures — degree,
closeness, eigenvector centrality and between-
ness — were applied to the largest component
subnetwork, and the authors were sorted and
ranked based on each of the measures. The top
15 authors across each measure were picked
Figure 4
Note: Opinion leaders are in red.
Figure 5
Frequency Distribution of Number of Coauthors per Publication
Author-Collaboration Network
0
20
40
60
80
100
0
200
400
600
800
1 3 5 7 9 11 13 15 17 19 21
Number of Authors per Publication
Frequency
Percent
24 26 29 34 1 3 5 7 9 11 13 15 17 19 21
Number of Authors
Frequency Distribution of Number
of Authors per Publication
Cumulative (%) Distribution of Number
of Authors per Publication
24 26 29 31

6. cognizant 20-20 insights 6
Note: Authors are represented by IDs across each centrality measure.
Figure 6
Top 15 Ranked Authors
Degree Closeness Eigenvector Betweenness
1 36 829 51 829
2 51 36 49 36
Author ID common to
two measures
3 491 655 524 216
4 295 2072 50 331
5 1471 749 56 749
Author ID common to
three measures
6 200 216 57 2072
7 293 491 502 200
8 331 1021 53 384
9 212 200 58 655
10 216 1227 450 475
11 384 295 1212 1227
12 592 1525 1093 491
13 749 564 2039 249
14 49 308 1090 2049
15 50 815 1094 637
(see Figure 6) and profile information for them
was collected from publicly available data. For
the sake of confidentiality, all authors have been
identified using network ID (or author ID) and
their names have been removed.
It may be noted here that in our case, the top
15 rankings of authors remain the same even if
the centrality measures are applied to the full
network.
Results
As highlighted in Figure 6, some listed authors
are among the top-ranked across two or three
measures. However, no author is ranked in the
top 15 across all four measures. The degree of
overlapping across measures may vary across
networks. A total of 40 unique author profiles
were identified in the union set of the top 15
across all the centrality measures. Profile infor-
mation was manually collected for each of the
ranked authors from publicly available data on
the Web.
The list includes eminent people who are regarded
as leaders in the field of genetics. The top authors
have an average work experience of 26 years. A
significant majority (60%) of the ranked authors
hold a doctorate degree and many of the authors
have multiple qualifications. These authors have
held leadership positions in important organiza-
tions related to medical genetics, such as the
National Society of Genetic Counselors and the
NCI Board of Scientific Advisors. Most also head
educational departments or programs in the
field of genetics within their academic institu-
tions. Many of the authors have led important
research programs or have worked as principal
investigators in research projects. Their contribu-
tions to the field of genetics, especially hereditary
diseases (which is a major focus of medical
genetics) and genetic counseling, have been
highly recognized; 24 authors (i.e., 60%) have
received one or more awards for their contribu-
tion to research and academics.
Based on an analysis of the collected informa-
tion, we found that 34 authors (i.e., 85%) could
be considered to be opinion leaders in the area
of medical genetics. Note: We could not ascertain
with absolute certainty whether the remaining
profiles could be considered to be thought leaders
or not, due to lack of information. For example,
one author was an assistant professor of genetics
and genomic sciences, but no other informa-
tion on work experience or research focus was
available. Similarly, we found two authors who
were certified genetic counselors, but additional
information was not available. Figure 7 offers a
snapshot of this profile information.

7. Profile Snapshot of 40 Authors Identified as Opinion Leaders
Qualifications*
Work Experience —
Summary
Advisory Board Memberships
Organizational
Affiliations
Key Contributions & Achievements
Country-wide Distribution
7%
Australia
10%
Canada
2%
France
3%
Italy
8%
UK
65%
U.S.
5%
The Netherlands
Average Years
of Experience*
At Least
24Authors Have Won
One or More Awards
 Majority of the authors hold very senior positions in the
academic community heading research groups or departments
within the institutions they work for e.g. Director of the
Graduate Program of Study in Genetic Counseling or Head,
Genetic Services Unit and Director, Genetic Counseling Train-
ing or Senior Researcher, Team Leader.
 A number of authors have led or worked on research projects
funded by federal or private organizations. Many of them
worked as Principal Investigators or Co-investigators.
A number of authors are key members of scientific or
medical advisory boards like:
 Scientific Advisory Board, Lynch Syndrome, Australia
 Advisory Panel, Center for Disease Control, U.S.
 National Human Genome Research Institute, U.S.
 Advisory Committee for New Genetics Services, Ontario
Ministry of Health and Long Term Care
 Medical Advisory Committee Birmingham, Alabama
The majority of the authors are affiliated
with key societies or organizations in the
area of genetics. Besides being members,
some of the authors have held leadership
roles in these bodies, including:
 Two past-presidents and one founding
member of National Society of Genetic
Counselors.
 One member of board of directors and one
president of American Board of Genetic
Counseling.
 Board members of Scientific Counselors,
National Human Genome Research
Institute.
 Inaugural chair of European Board of
Medical Genetics.
 One past-president and one founding
member of American College of Medical
Genetics and Genomics.
Listed below are some of the key contributions and achievements of the 40 authors.
We have deliberately removed dates and specifics to keep the authors anonymous.
 One of the first and key contributors to hereditary cancer research.
Has a cancer related syndrome named after him.
 Played a major role in isolating the genes for both Huntington’s
disease and myotonic dystrophy and built comprehensive genetic
counseling service for the whole of Wales.
 Played a major role in the development of national guidelines in
genetics (American College of Medical Genetics).
 Part of the team that discovered BRCA1 and BRCA2 — one of the most
important breakthroughs in cancer research.
 Involved in the EuroGentest2 European project to improve the stan-
dard of genetic counseling, particularly regarding genetic testing.
 Spearheaded a multi-million-dollar genomic medicine initiative, known
as the Genomedical Connection.
 International Award, National Society of Genetic Counselors (U.S.)
 Advanced Medical Leader Award from the British Association of
Medical Managers
 Strategic Leader Award, National Society of Genetic Counselors (NSGC)
 “Natalie Weissberger Paul Lifetime Achievement Award” from the
National Society of Genetic Counselors
 American Cancer Society’s Distinguished Service Award
 Canadian Gene Cure Foundation — Champion of Genetics
 American Academy on Physician and Patient Award for outstanding
research contribution
* Work-ex data available for
33 of the 40 authors* Many authors have multiple
qualifications
0
5
10
15
20
25
Certified
Genetic
Counselor
MD PhD
10 7 24
26
7cognizant 20-20 insights
Figure 7
Ranked Author Profile Snapshots

8. 8cognizant 20-20 insights
Looking Forward
A simple application of SNA to a scientific pub-
lications network yielded highly relevant results
for us. It is possible to apply the same approach
to other therapeutic areas.
Besides being empirically sound and objective,
SNA offers the potential for life sciences
companies (in fact, any organization) to generate
a great deal of KOL insights. Many industries are
already exploring how they can apply SNA to
numerous opportunities as varied as ferreting out
malinvestment (including fraud as well as funding
for terrorist networks), to identifying key influenc-
ers in an online social network for targeted digital
advertising. Going forward, we may see stronger
adoption of SNA within life sciences, especially
for identifying KOLs (see sidebar, page 9).
Next Steps
We recommend that life sciences companies
consider the following before embarking on their
SNA journeys:
• Network attributes: Our analysis focused on
a simple collaboration relationship among the
authors or researchers. More comprehensive
and complex analysis could involve various
attributes of the network. For example, analysis
may include attributes of the network members
such as experience, location, academic qualifi-
cations and number of publications.
• Weighted networks: Our analysis did not
assign any weights to the relationships
between members. In other words, all relation-
ship ties were considered equally important.
However, weighted networks are possible
based on variables such as geographical
location of members, frequency of collabora-
tion or strength of a relationship.
• Dynamic networks: Much recent study related
to SNA has focused on dynamic networks
(i.e., how a network evolves with time and
accounting for time-based attributes). For
example, considering our network, we could
have assigned weights to the participation of
network members with recent contributions
being assigned higher value than older ones.

9. 9cognizant 20-20 insights
Scenario 1: A large pharma company wants to leverage
external research to develop a novel drug for obesity. The
company hopes that the identified experts will contribute
toward the entire drug development process – from prede-
velopment to post-approval feedback analysis.
• Challenge(s): Identifying the best experts in the given
therapeutic area is a huge challenge. While prior
research work and contributions to publications are
important criteria, the pharma company is well aware of
the fact that quality of contribution is more important
than quantity when it comes to research. Considering
only numerical criteria such as the number of publica-
tions or conferences attended won’t suffice.
• Approach: It is a well-accepted observation that people
prefer to work with their reputed peers. As mentioned
above, social networks model social prestige. The
company collected scientific publications that focused on
research specific to its drug area. A network of coauthors
could be constructed, with additional weights assigned
based on frequency as well as recency of collaboration.
SNA measures could then be applied to the network
to identify KOLs. This could be followed by further
scrutiny of the prior work of the shortlisted profiles, and
interviews could be conducted to narrow down the list of
KOLs selected for research collaboration.
Scenario 2: A large pharma company wants to improve the
adoption rate for a recently launched drug in a particular
market. The drug is used to treat a particular chronic
condition, and two competing drugs have been available
for the same treatment for about a year. The company
believes that engaging KOLs will increase the rate of
adoption of the drug.
• Challenge(s): Given that the drug category relates to a
chronic condition, drug efficacy is not easily observable.
As such, there is uncertainty in the physician community
regarding the best treatment option. The lack of com-
parative data makes the role of KOLs in adoption of a
drug even more significant and the task of identifying
the KOLs even more relevant.
• Approach: Typically, the level of detailing required for
a physician is proportional to his or her prescription
volume. However, this approach is not always effective.
The company could take an alternative approach by
identifying physicians in the target market who have
prescribed drugs that belong to the specific drug
category. A market survey could be conducted among
these physicians, requesting that they identify other
physicians with whom they consulted for guidance or
with whom they discussed treatment-related issues.
Based on collected responses, a social network could
be created that represented “who influenced whom.”
SNA could be used to analyze this network and identify
highly influential physicians. This could then be cross-
referenced with product detailing data which could tell
if influential physicians received appropriate detailing.
The company could then move quickly to remedy the
situation.
Applying Social Network Analysis
The following hypothetical scenarios are designed to provide a taste of how SNA can be
leveraged by organizations.

10. cognizant 20-20 insights 10
Appendix
Social Network Analysis Measures
Once a network has been constructed, various types of analysis can be applied. This ranges from simple
network metrics, such as graph density and centrality, to complex ones related to identifying communi-
ties within a network. Widely used centrality measures include:
Centrality measures: One of the most popular measures used in SNA to identify key players is centrality.
It is used to determine how important a node is to a network. The four most popular centrality measures
are degree, closeness, betweenness and eigenvector centrality. Of these, the first three were formalized
by Freeman10
and the fourth measure, eigenvector, by Bonacich.11
• Degree of a node is the number of edges incident on that node. For a network with “n” nodes, the
maximum number of edges to a node is “n-1.” The normalized degree for a node “i” is explained by
the following formula:
h h
• Betweenness centrality of an actor or node is a measure of how often an actor lies on the shortest
path between two other actors or nodes. It measures the degree to which a particular node controls
the flow of information or resources between other nodes in a network. This measure assumes that
information flows in a network along the shortest paths. If pkj represents the number of possible
shortest paths between two nodes k and j and pkij the number of shortest paths between k and j that
include node i, then the normalized value of betweenness centrality for node “i” is explained by the
following formula:
( )
• Closeness is a measure of being as close to as many actors as possible. McGuire explains closeness
as, “in this position, an actor needs to rely on the fewest number of people to send or receive a
resource flowing in the network.”5
The normalized measure of closeness centrality for a node “i” is given by:
h h h
For a network that is not fully connected, i.e., if nodes i and j are not connected, dij cannot be defined
and hence closeness centrality cannot be calculated.5
Some tools and libraries take the distance be-
tween two unconnected nodes as a large finite value (e.g., “n,” the number of nodes in the network).
Opsahl12
proposed an alternative closeness measure as:
• Eigenvector centrality is defined as the principal eigenvector of the sociomatrix of the social
network.11
This measure signifies how well connected a node is to other nodes that are themselves
highly connected. Therefore, even if a node may not have many adjacent nodes, if the nodes adjacent
to it are well connected then the node will have a high Eigen centrality value. The eigenvector V of a
network is defined as:
h h h

11. cognizant 20-20 insights 11
Footnotes
1 S. Sismondo, “Key Opinion Leaders and the Corruption of Medical Knowledge: What the Sunshine Act Will
and Won’t Cast Light On,” The Journal of Law, Medicine and Ethics, pp. 635-43, 2013 Fall.
2 R. D. Putnam, “Bowling alone: America’s declining social capital,” Journal of Democracy, pp. 65-78, 1995.
3 Lnx Pharma, “Quantity Does Not Equal Quality in Evaluating a Scientist’s Real Importance as a Key Opinion
Leader,” 2010.
4 J. Moreno, “Who Shall Survive? A New Approach to the Problem of Human Inter-relations,” 1953.
5 R. M. McGuire, “’Weighted Key Player Problem for Social Network Analysis’, Air Force Institute of
Technology,” 2011.
6 Medical genetics is the application of genetics to medical care. It focuses on diagnosis, management and
counseling of individuals with genetic disorders including hereditary diseases. http://en.wikipedia.org/wiki/
Medical_genetics.
7 PubMed comprises over 24 million citations for biomedical literature from MEDLINE, life science journals
and online books. http://www.ncbi.nlm.nih.gov/pubmed/.
8 igraph is a library for network analysis. http://cran.r-project.org/web/packages/igraph/index.html.
9 R is a free software environment for statistical computing and graphics. http://www.r-project.org/.
10 L. Freeman, “Centrality in networks: Conceptual clarification,” Social Networks, pp. 1:215-239, 1979.
11 P. Bonacich, “Factoring and weighting approaches to status scores and clique identification,” Journal of
Mathematical Sociology, pp. 2:113-120, 1972.
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12. About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business
process outsourcing services, dedicated to helping the world’s leading companies build stronger busi-
nesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfac-
tion, technology innovation, deep industry and business process expertise, and a global, collaborative
workforce that embodies the future of work. With over 100 development and delivery centers worldwide
and approximately 217,700 employees as of March 31, 2015, Cognizant is a member of the NASDAQ-100,
the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and
fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.
World Headquarters
500 Frank W. Burr Blvd.
Teaneck, NJ 07666 USA
Phone: +1 201 801 0233
Fax: +1 201 801 0243
Toll Free: +1 888 937 3277
Email: inquiry@cognizant.com
European Headquarters
1 Kingdom Street
Paddington Central
London W2 6BD
Phone: +44 (0) 20 7297 7600
Fax: +44 (0) 20 7121 0102
Email: infouk@cognizant.com
India Operations Headquarters
#5/535, Old Mahabalipuram Road
Okkiyam Pettai, Thoraipakkam
Chennai, 600 096 India
Phone: +91 (0) 44 4209 6000
Fax: +91 (0) 44 4209 6060
Email: inquiryindia@cognizant.com
­­© Copyright 2015, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is
subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.
Codex 1234
About the Author
Amoy Kumar Dutta is a Consultant within Cognizant Business Consulting’s Enterprise Applications
Services (EAS) Practice. He has over six years of industry experience and specializes in business strategy,
customer relationship management consulting, sales and marketing process consulting and business
development across the life sciences, travel, telecommunications and entertainment industries. He can
be reached at Amoykumar.Dutta@cognizant.com.