social network analysis interdisciplinary approaches and case studies

Social Network
Analysis
Interdisciplinary Approaches and Case Studies



Social Network
Analysis
Interdisciplinary Approaches and Case Studies

Edited by

Xiaoming Fu • Jar-Der Luo • Margarete Boos


CRC Press
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Contents
Foreword.............................................................................................................vii
Preface.................................................................................................................ix
Editors.................................................................................................................xi
Contributors......................................................................................................xiii

PART I METHODOLOGIES FOR INTERDISCIPLINARY
SOCIAL NETWORK RESEARCH
1 Methods for Interdisciplinary Social Network Studies...........................3
XIAOMING FU, JAR-DER LUO, AND MARGARETE BOOS

2 Towards Transdisciplinary Collaboration between Computer and

Social Scientists: Initial Experiences and Reflections...........................21

DMYTRO KARAMSHUK, MLADEN PUPAVAC, FRANCES SHAW,
JULIE BROWNLIE, VANESSA PUPAVAC, AND NISHANTH SASTRY

3 How Much Sharing Is Enough? Cognitive Patterns in Building

Interdisciplinary Collaborations...........................................................41
LIANGHAO DAI AND MARGARETE BOOS

PART II  SOCIAL NETWORK STRUCTURE
4 Measurement of Guanxi Circles: Using Qualitative Study to

Modify Quantitative Measurement.......................................................73
JAR-DER LUO, XIAO HAN, RONALD BURT, CHAOWEN ZHOU,
MENG-YU CHENG, AND XIAOMING FU

5 Analysis and Prediction of Triadic Closure in Online Social

Networks.............................................................................................105
HONG HUANG, JIE TANG, LU LIU, JAR-DER LUO, AND XIAOMING FU

6 Prediction of Venture Capital Coinvestment Based on Structural

Balance Theory....................................................................................137
YUN ZHOU, ZHIYUAN WANG, JIE TANG, AND JAR-DER LUO

v


vi  ◾ Contents

7 Repeated Cooperation Matters: An Analysis of Syndication in the

Chinese VC Industry by ERGM..........................................................177
JAR-DER LUO, RUIQI LI, FANGDA FAN, AND JIE TANG

PART III  SOCIAL NETWORK BEHAVIORS
8 Patterns of Group Movement on a Virtual Playfield: Empirical

and Simulation Approaches.................................................................197
MARGARETE BOOS, WENZHONG LI, AND JOHANNES PRITZ

9 Social Spammer and Spam Message Detection in an Online

Social Network: A Codetection Approach...........................................225
FANGZHAO WU AND YONGFENG HUANG

PART IV SOCIAL NETWORKS AS COMPLEX
SYSTEMS AND THEIR APPLICATIONS
10 Cultural Anthropology through the Lens of Wikipedia.....................245
PETER A. GLOOR, JOAO MARCOS, PATRICK M. DE BOER, HAUKE
FUEHRES, WEI LO, AND KEIICHI NEMOTO

11 From Social Networks to Time Series: Methods and Applications........269
TONGFENG WENG, YAOFENG ZHANG, AND PAN HUI

12 Population Growth in Online Social Networks..................................285
KONGLIN ZHU, XIAOMING FU, WENZHONG LI, SANGLU LU, AND
JAN NAGLER

PART V COLLABORATION AND INFORMATION

DISSEMINATION IN SOCIAL NETWORKS
13 Information Dissemination in Social-Featured Opportunistic

Networks.............................................................................................309
WENZHONG LI, SANGLU LU, KONGLIN ZHU, XIAO CHEN,
JAN NAGLER AND XIAOMING FU

14 Information Flows in Patient-Oriented Online Media and

Scientific Research.............................................................................. 343
PHILIP MAKEDONSKI, TIM FRIEDE, JENS GRABOWSKI,
JANKA KOSCHACK, AND WOLFGANG HIMMEL

15 Mining Big Data for Analyzing and Simulating Collaboration

Factors Influencing Software Development Decisions........................367
PHILIP MAKEDONSKI, VERENA HERBOLD, STEFFEN HERBOLD,
DANIEL HONSEL, JENS GRABOWSKI, AND STEPHAN WAACK

Index............................................................................................................387


Foreword
Social network analysis has had a rich history as an intellectual enterprise. Since
its inception in the 1930s and 1940s, it has made significant methodological and
theoretical contributions to the analysis of social relations from microscopic relations to macroscopic systems of social networks. Initially employed to study dyadic
relations and small social groups and communities, the scope of analysis and the
participation of scholars have expanded significantly since the 1960s and 1970s as
computers emerged as tools for analyzing larger social systems. Now, participating
scholars come from a variety of disciplines, ranging from sociology, social psychology, anthropology, political science, business and management sciences, and

other social and behavioral sciences to computer science, complex systems, statistics, and information and communication sciences. Interdisciplinary exchanges
have become possible in many national, regional, and international meetings (e.g.,
most notably the annual meetings of the International Network for Social Network
Analysis) and in the publications in journals (e.g., Social Networks) and in books
and monographs.
Yet, most of the presentations, papers, and books have continued to be authored
by scholars in a single discipline or at most two to three allied disciplines (e.g., sociology, management science, and social psychology). What have been lacking are
truly collaborative efforts where skills and knowledge across disciplines, especially
crossing the social science–computer science boundary, are brought together in
advancing the methodology and theory.
The impetus for such collaborations gains momentum with the recent development and availability of Big Data, which begin to yield relationships in the cyberspace, hitherto undetected. As more computer scientists join in to mine such data,
the realization of the need for substantive and strategic analyses propels more interest in dialogues between computer scientists and social and behavioral scientists.
Such collaborations go beyond disciplinary boundaries, as typically scholars are
bounded in their normative communities and media of presentation and publications. It would require extraordinary efforts on the part of scientists to cross such
boundaries to bring such collaborations to fruition. It would also require the participation of outstanding scholars from their respective fields to advance knowledge
in such collaborations.
vii


viii  ◾ Foreword

It is, therefore, truly extraordinary to see such efforts and opportunities to have
taken place when computer scientist, Xiaoming Fu, who has developed his distinguished career cross and beyond national boundaries of China and Germany, has
sought and found collaborators in social sciences in China, Jar-der Luo, a sociologist, and in Germany, Margarete Boos, a social psychologist. They have brought
their distinguished scholarships together, along with their colleagues, to create a
book that demonstrates the utility of such collaborations in advancing the methodologies and in bringing about a deeper understanding of social structures, network behaviors, networks as complex systems, and collaborations and information
dissemination in social networks. The book illustrates exemplary efforts and fruition in truly integrative collaborations between computer scientists and social and
behavioral scientists. It has set a high benchmark for all such cross-disciplinary
collaborations to come and has brought social network analysis to new heights.
Nan Lin

Professor of Sociology
Duke University
Durham, North Carolina


Preface
The roots of this book depict the genesis of a successful interdisciplinary, East–West
academia cooperation. The book project sprung from an ongoing effort among a
handful of scientists in China and Germany, following leaders of Nanjing University
and the University of Göttingen having visited their respective cities in 2009. One
of the originating authors, who had been involved in these visits and was shortly
later appointed as a visiting chair professor at Tsinghua University, had the idea of
an interdisciplinary collaboration on social network analysis between the countries’
universities. To find the right sociologist in China interested in social network analysis, the coauthor phoned the university president’s office of Tsinghua University
and then Tsinghua University’s research department head, dean of the School of
Humanities and Social Sciences, and chair of the Sociology Department—who
organized an introduction to an interested sociologist and eventually a contributing author to this book. At that time, yet another of the book’s collaborators,
who was from Nanjing University’s Computer Science Department, was visiting
the originating author’s group at the University of Göttingen for a collaboration
on the topic of mobile social networks with researchers within the university’s
Department of Social and Communication Psychology. As a result, the head of
the said department, together with other scientists and leaders at the University of
Göttingen, Nanjing University, and Tsinghua University, entered into discussions
that developed into an organized Sino–German interdisciplinary collaboration on
the broader domain of social networks. This intercultural, interdisciplinary collaboration took the form of several lectures, seminars, and annual workshops as well
as several jointly supervised bachelor’s degree, master’s degree, and PhD students at
Tsinghua University, Nanjing University, and the University of Göttingen.
A member of CRC Press eventually approached these collaborators for a possible book on some of the Sino–German interdisciplinary collaborations on social
network analysis. We were given the freedom to organize the book’s content, style,
and format. In addition to solicitations for authoring book chapters from the three

universities, a couple of international authors from the United Kingdom and the
United States were invited and contributed several interesting chapters.
People are linked in social networks when they interact with their families,
friends, colleagues, and other individuals and groups who share common interests
ix


x  ◾ Preface

and goals. Links in social networks are based on various reasons, which can range
from family ties to the need for technical or business information transfer or other
sorts of interdependencies. Today, social networks are highly dynamic entities, as
they are fueled by open access to modern information and communication technologies and high geographic mobility, resulting in ever-increasing interpersonal
and interdisciplinary interactions and collaborations.
This book will interest readers looking to learn more about new methods and
techniques that are synthesized from the different research disciplines involved in
the formation, analysis, and modeling of various traditional and digital social networks as well as their applications.
We have organized the book chapters into five clusters according to the following aspects:
◾◾ Methodologies for interdisciplinary social network research (Chapters 1
through 3)
◾◾ Social network structure (Chapters 4 through 7)
◾◾ Social network behaviors (Chapters 8 through 10)
◾◾ Social networks as complex systems and their applications (Chapters 11
and 12)
◾◾ Collaboration and information dissemination in social networks (Chapters
13 through 15)
We express our gratitude to the leaders of Nanjing University and Tsinghua
University and especially to the University of Göttingen for ultimately making
the publication of this book possible. We also thank the contributing authors who,
as interdisciplinary collaborators often do, added the task of contributing to this

collaboration to their already overextended schedule. We extend special thanks to
Ruijun He at CRC Press and Taylor & Francis Group for his enduring patience as
our editor and to the project coordinator, Amber Donley, in dealing with editorial
matters such as layout and graphics, and a hearty thank-you to the support staff
too numerous to mention. Without their help, this book edition would not have
been possible.
Xiaoming Fu
Jar-Der Luo
Margarete Boos


Editors
Xiaoming Fu is a full professor of computer science and head of the Computer
Networks Group at the Institute of Computer Science, University of Göttingen,
Germany. He is also founding director of the Sino–German Institute of Social
Computing, University of Göttingen. His research interests include Internet-based
systems, protocols, and applications, including social networks. Professor Xiaoming
holds a PhD in computer science from Tsinghua University, China. He is an IEEE
distinguished lecturer and has served as secretary and then vice chair of the IEEE
Communications Society Technical Committee on Computer Communications
and chair of the Internet Technical Committee, the joint committee of the IEEE
Communications Society and the Internet Society.
Jar-Der Luo is a professor at the Sociology Department, Tsinghua University in
Beijing, China; he is also president of the Chinese Network for Social Network
Studies and director of Tsinghua Social Network Research Center. He received
his PhD in sociology from Stony Brook University in New York, supervised by
Mark Granovetter. His researches cover numerous topics in social network studies, including social capital, trust, social network in Big Data, self-organization
process, and Chinese indigenous management, such as guanxi, guanxi circle, and
favor exchange.
Margarete Boos is a full professor of psychology and head of the Department of

Social and Communication Psychology at the Institute for Psychology, University
of Göttingen, Germany. Her research focuses on group psychology, especially
coordination and leadership in teams, computer-mediated communication, and
distributed teams, as well as methods for interaction and communication analysis. She holds a PhD in sociology. She applies her research methods and results
to team diagnostics and team training and founded the start-up Malamut Team
Catalyst GmbH together with colleagues in 2010. She developed the Göttingen
Civil Courage Training and puts it into practice as a train-the-trainer concept in
many institutions.

xi



Contributors
Margarete Boos
Institute of Psychology
and
Sino-German Institute of Social
Computing
University of Göttingen
Göttingen, Germany
Julie Brownlie
School of Social and Political Sciences
University of Edinburgh
Edinburgh, United Kingdom
Ronald Burt
Booth School of Business
and
Department of Sociology
University of Chicago

Chicago, Illinois
Xiao Chen
Department of Computer Science
Texas State University
San Marcos, Texas
Meng-Yu Cheng
Department of Business
Administration
Feng-Chia University
Taichung, Taiwan, China

Lianghao Dai
Institute of Psychology
University of Göttingen
Göttingen, Germany
Patrick M. De Boer
Department of Computer Science
University of Zurich
Zürich, Switzerland
Fangda Fan
Department of Biostatistics
University of Illinois at Chicago
Chicago, Illinois
Tim Friede
Department of Medical Statistics
University of Göttingen
Göttingen, Germany
Xiaoming Fu
Institute of Computer Science
and

Sino-German Institute of Social
Computing
University of Göttingen
Göttingen, Germany
Hauke Fuehres
Galaxyadvisors AG
Aarau, Switzerland
xiii


xiv  ◾ Contributors

Peter A. Gloor
Center for Collective Intelligence
Sloan School of Management
Massachusetts Institute of Technology
Cambridge, Massachusetts
Jens Grabowski
Institute of Computer Science
and
Sino-German Institute of Social
Computing
University of Göttingen
Göttingen, Germany
Xiao Han
Business School
Shanghai University of Finance and
Economics
Shanghai, China
Steffen Herbold

Institute of Computer Science
University of Göttingen
Göttingen, Germany
Wolfgang Himmel
Department of General Practice
University Medical Center
University of Göttingen
Göttingen, Germany
Daniel Honsel
Institute of Computer Science
University of Göttingen
Göttingen, Germany
Verena Herbold
Institute of Computer Science
University of Göttingen
Göttingen, Germany
Hong Huang
Institute of Computer Science
University of Göttingen
Göttingen, Germany

Yongfeng Huang
Department of Electronic Engineering
Tsinghua University
Beijing, China
Pan Hui
Department of Computer Science and
Engineering
Hong Kong University of Science and
Technology

Clear Water Bay, Hong Kong
Dmytro Karamshuk
Department of Informatics
King’s College London
London, United Kingdom
Janka Koschack
Department of General Practice
University Medical Center
University of Göttingen
Göttingen, Germany
Ruiqi Li
School of Systems Science
Beijing Normal University
Beijing, China
Wenzhong Li
State Key Laboratory for Novel
Software Technology
Department of Computer Science and
Technology
and
Sino-German Institute of Social
Computing
Nanjing University
Nanjing, Jiangsu, China
Lu Liu
TangoMe Inc.
Mountain View, California


Contributors  ◾  xv

Wei Lo
Department of Computer Science
Zhejiang University
Hangzhou, Zhejiang, China
Sanglu Lu
State Key Laboratory for Novel
Software Technology
Department of Computer Science and
Technology
and
Sino-German Institutes of Social
Computing
Nanjing University
Nanjing, Jiangsu, China
Jar-Der Luo
Department of Sociology
and
Center for Social Network Research
Tsinghua University
Beijing, China
Philip Makedonski
Institute of Computer Sciences
University of Göttingen
Göttingen, Germany
Joao Marcos
Galaxyadvisors AG
Aarau, Switzerland
Jan Nagler
Computational Physics for Engineering

Materials, IfB
ETH Zurich
Zurich, Switzerland
and
MPI for Dynamics and
Self-Organization
Göttingen, Germany

Keiichi Nemoto
Fuji Xerox Co., Ltd.
Yokohama-shi, Kanagawa, Japan
Johannes Pritz
Courant Research Centre Evolution of
Social Behaviour
University of Göttingen
Göttingen, Germany
Mladen Pupavac
School of Politics and International
Relations
University of Nottingham
Nottingham, United Kingdom
Vanessa Pupavac
School of Politics and International
Relations
University of Nottingham
Nottingham, United Kingdom
Nishanth Sastry
Department of Informatics
King’s College London
London, United Kingdom

Frances Shaw
School of Social and Political Sciences
University of Edinburgh
Edinburgh, United Kingdom
Jie Tang
Department of Computer Science and
Technology
and
Center for Social Network Research
Tsinghua University
Beijing, China
Stephan Waack
Institute of Computer Science
University of Göttingen
Göttingen, Germany


xvi  ◾ Contributors

Zhiyuan Wang
School of Computer
National University of Defense
Technology
Changsha, Hunan, China

Chaowen Zhou
Department of Sociology
Tsinghua University
Beijing, China

Tongfeng Weng
Department of Computer Science and
Engineering
Hong Kong University of Science and
Technology
Clear Water Bay, Hong Kong

Yun Zhou
School of Computer
National University of Defense
Technology
Changsha, Hunan, China

Fangzhao Wu
Department of Electronic Engineering
Tsinghua University
Beijing, China
Yaofeng Zhang
Department of Computer Science and
Engineering
Hong Kong University of Science and
Technology
Clear Water Bay, Hong Kong

Konglin Zhu
School of Information and
Communication Engineering
Beijing University of Posts and
Telecommunications
Beijing, China

and
Sino-German Institute of Social
Computing
University of Göttingen
Göttingen, Germany


METHODOLOGIES
FOR
INTERDISCIPLINARY
SOCIAL NETWORK
RESEARCH

I



Chapter 1

Methods for
Interdisciplinary Social
Network Studies
Xiaoming Fu, Jar-Der Luo, and Margarete Boos
Contents
1.1Introduction..................................................................................................4
1.2 Methodology for Combining Big Data Mining and Qualitative
Studies in Theory Building............................................................................5
1.3A Tour of Interdisciplinary Approaches and Case Studies
Presented in this Book...................................................................................9
1.3.1 Part I: Methodologies for Interdisciplinary Social

Network Research...........................................................................11
1.3.2 Part II: Social Network Structure....................................................12
1.3.3 Part III: Social Network Behaviors..................................................15
1.3.4 Part IV: Social Networks as Complex Systems and
Their Applications............................................................................15
1.3.5 Part V: Collaboration and Information Dissemination
in Social Networks...........................................................................17
References............................................................................................................18

3


4  ◾  Social Network Analysis

1.1 Introduction
People participate in social networks when they interact with their families, friends,
colleagues, and other individuals or groups. Social networks link people together
via a common interest and/or other kinds of interdependencies. Today, the dynamics of social networks are often fueled by access to modern online platforms and
high geographic/spatial mobility, resulting in greater interpersonal interaction. For
example, Facebook, the most widely used online social networking service as of
this writing, reported 1.79 billion (including 1.66 billion mobile) monthly active
users as of September 30, 2016 (Facebook, n.d.). China’s Tencent, one of the largest
Internet companies in the world whose subsidiaries provide, among other services,
instant messaging (Tencent QQ) and the mobile chat service WeChat, reported 1.1
billion registered WeChat users as of January 22, 2015, and 570 million daily active
WeChat users as of November 5, 2015 (DMR, n.d.). Social networks—whether
they be online or real world—are of vital importance to modern societies in that
they influence daily work, contacts, and leisure activities. Social networks enable
interactions for collaborating, learning, and information dissemination within
physical (i.e., real world) or virtual (e.g., online) social networks.

A social network is composed of individual nodes (persons, teams, or organizations) and the ties (also called relationships, connections, edges, or links)
between these individual nodes. Together these form a graph-based structure
that is often complex (see e.g., Barabasi, 2003). Given the widespread presence
of online social networks and also real-world networks, it is interesting to understand how a tie is created; how the network functions; what its structure looks
like; and how it evolves, stabilizes, adapts, and changes. For practical cases and
applications, we need to know how these features can be leveraged, such as how
to bring together the strengths of diverse technical or scientific disciplines in
creative collaboration, to make business or political decisions, and to develop
risk-reducing measures to mitigate or control risk, for instance, in epidemics or
stock markets, or even to curtail rumors/spam. This book intends to present new
methods and techniques that are synthesized from different research disciplines
involved in the formation, analysis, and modeling of various social networks as
well as their applications.
Most existing studies on social networks (e.g., Milgram, 1967; Freeman, 2004)
either study the network as a whole regarding its structure with specific relationships in the defined population, or the network from an individual perspective (so-called egocentered networks). Many have also studied the consequences
for individuals who are embedded in social relations and networks, focusing,
for example, on the effects in terms of receiving social support or finding a job
(e.g., Granovetter, 1973). Physicists; social, behavioral, and epidemic researchers;
and practitioners have developed and collected a large body of hypotheses, models, and empirical findings on the structure, processes, and consequences of social
networks, both real word and online. In the last decade, online social networks


Methods for Interdisciplinary Social Network Studies  ◾  5

have gained particular importance in everyday life due to their facilitation of the
intercommunication (i.e., social networking) among a rising share of the population in modern societies. Indeed, the new forms of online social networks open up
vast opportunities for studying social networks. Most networks that were studied
in the social science domain were targeted at small groups, due to financial and
practical limitations in accessing the data (Gjoka et al., 2010). Barriers that once
made physical social networks inaccessible have now been overcome as a result of

the emergence of big data storage, processing and traffic-managing capacities, and
numerous social media and other online platforms. However, existing work among
the so-called nodes of social networks—persons, teams, and organizations—does
not yet take full advantage of the opportunities provided through interdisciplinary
studies, which remains generally confined to specific fields. The result is a more
intra- than interdisciplinary focus with limited advances. Interdisciplinary cooperation between social, behavioral, and epidemiological research, on one hand, and
physics and computer science, on the other hand, holds the promise of enormous
advances in the analysis of the potential of online social networks, and that of largescale social networks in general.
We are pleased to witness a handful of researchers working with people from
different disciplines, developing and employing various methodical approaches
for studying complex social networks. A subset of such efforts is included in this
book. These projects have been carried out in the form of close interdisciplinary
collaborations by researchers with backgrounds in complex systems, statistics, and
computer sciences, together with medical, management, behavioral, and social
sciences, who continue to develop methods for data mining, network analysis,
theory building, and more generally the interdisciplinary social network analysis
methodologies.
By interlinking the expertise from divergent disciplines, new results and considerable progress are achievable in social network studies, as evidenced by the results
reported in this book. Although a small set of chapters were written by scientists
from the same discipline, knowledge and experiences from other disciplines were
adopted and exploited in these chapters, constituting a broader sense of hybrid
intra- and interdisciplinarity.

1.2 Methodology for Combining Big Data Mining
and Qualitative Studies in Theory Building
This section will begin with a methodology developed during several case studies
(e.g., see Chapters 4 through 7). In short, this methodology starts with quantitative
studies, mining sample data with selected hypotheses (based on preliminary knowledge gained from a literature review), followed by qualitative analysis (e.g., through
sociological interviews and questionnaires) towards ground truthing; based on this,
predictions about certain network properties, patterns, or indicators can be made.

6  ◾  Social Network Analysis

By iterating this process, which integrates qualitative and quantitative studies, several times, hypotheses can be tested and new models may be established or existing
models refined.
Before going into details about the methodology, we briefly explain several
terms that are frequently used in this book:
◾◾ Big data: data collected from the online world or other digitalized sources
that are too complex or of a too huge volume to be analyzed by traditional
data processing tools
◾◾ Small data: structured data collected from quantitative surveys performed in
the real world or extracted from big data
◾◾ Complex system: a system consisting of elements plus the interactions between
these elements
◾◾ Data mining: the process of finding predictors for a social phenomenon with
little or no guidance of theories; in other words, extracting potentially useful
(but yet-to-be-empirically-validated) patterns from data sources, for example,
databases, texts, the web, images, etc.
◾◾ Ground truth: level of accuracy of the training set reflecting or approximating
the real world or population under investigation
◾◾ Ground truthing: the process of garnering sufficiently representative data that
reflects/approximates the real case
◾◾ Hypothesis testing: the process of designing an empirical study apt to falsify a
hypothesis derived from theory
◾◾ Machine learning: similar to how humans learn from past experience, a computer (i.e., machine) system learns from data that represent some “past experiences” of the applied domain
◾◾ Qualitative approach: includes typical sociological methods such as interviewing, field observations, open questions’ surveys, case studies, etc., which offer
a way for hypothesis testing
◾◾ Quantitative approach: includes data mining and hypothesis testing based on
structured and/or big data

◾◾ Real-world social networks: physical networks (e.g., families, teams, and
organizations)
◾◾ Online and other virtual social networks: social networks that are media based
(Internet, satellite, cell, Wi-Fi, computer, etc.)
◾◾ Supervised learning: method of labeling prior available example data (so-called
training sets composed of observations, measurements, etc.) with predefined
classes, which are used to train a model or algorithm to classify new data/
instances into ones of the predefined classes
◾◾ Theoretical model: a theoretical mechanism that explains how explanatory
variables influence the target social phenomenon
◾◾ Modeling: a process of developing a theoretical model for testing against
quantitative data


Methods for Interdisciplinary Social Network Studies  ◾  7

◾◾ Theory developing/building: a process that begins with intuitions or interpretations (articulated as hypotheses), for example, on data mining results, then
gives the reasoning behind the intuitions or interpretations, building a model
based on said reasoning, defining the variables in the model, and collecting
data from the real world to test the model in order to test the theory
◾◾ Survey: a method for collecting quantitative information about items in a
population (Creswell, 2013)
◾◾ Interview: a conversation between two or more people where questions are
asked by the interviewer to elicit facts or statements from the interviewee
(Creswell, 2013)
◾◾ Sampling: selection of observations to acquire some knowledge of a statistical
population (Creswell, 2013)
◾◾ Sampling bias: a bias in which a sample is collected in such a way that some
members of the intended population are less likely to be included than others
(Creswell, 2013)

The methodology of a research cycle in social network research often begins with
mining of online data, with the expectation that some interesting social phenomena will be identified. We then interpret these findings by way of either a comparison with existing theories and/or by creating our own preliminary theory. Using
preexisting theories and/or our own preliminary theory as a guide, various qualitative methods, such as interviews, field observations, open questions surveys, case
studies, etc., can be used. Qualitative studies provide us with an understanding of
ground truth, which can be used to test the findings and interpretations derived
from data mining. Through the combination of ground truth, existing theories,
and/or our preliminary theory, a base for theory building and hypothesis development is established. Then a model based on the operative theory is built in order
to predict new facts, and more sets of data are collected for testing the theoretical
model. Oftentimes, there are ground truths checked by surveys in the real world
that do not jibe with our interpretation of the results of data mining, and/or
further examination of initial qualitative studies reveals further observations not
accessible through the findings and interpretations gained from the first-stage data
mining. This will lead to a second run of data mining and qualitative studies. This
process is illustrated in Figure 1.1.
The whole process of theory development concerning a social phenomenon
includes several runs of data mining, interpretation, qualitative studies, and model
building. Online big data opens up a new world for mining social science data upon
which to build theories and for testing hypotheses to confirm theories. However,
without checking the ground truth of online-mined data against real-world qualitative studies and quantitative surveys, the mining of online data remains invalidated
and therefore largely useless.
Taking Chapter 7 as an example, where data about cooperation networks in
the Chinese venture capital (VC) industry (based on the SiMuTon database) are


8  ◾  Social Network Analysis

Data mining of
online data

Interpretation of

qualitative studies’
dialogue with the
existing theories

Building theoretical
model predicting
new facts

Figure 1.1  A cycle of the dialogue between data mining and theory development.

explored, the authors try to understand the relational circle of leading companies
in this industry. Just like a Dunbar circle (Dunbar, 1992), an industry leader has
several layers of partners in his/her egocentered network, differentiated by the frequency of their cooperation. A high cooperation frequency indicates a strong tie
between two partners. Analyses of this industrial network using the exponential
random graph model (ERGM) (Frank and Strauss, 1986; Wasserman and Pattison,
1996) show that different layers of partnership are separated by the following frequencies of cooperation: 2, 4, and 7 or 8. This result poses the following questions:
Is this finding true? What is the meaning of the thresholds that separate cooperation ties of different strengths? For example, what makes cooperating once different from cooperating twice? Qualitative studies allow us to answer these questions
by providing detailed information concerning a VC firm’s behavior and motivations, while quantitative studies provide an overall picture of an industry and the
average behaviors of different types of VC firms. Both of them are important for
investigating a VC firm’s syndication network and the motivation behind the networking behaviors.
The mixed approach through the dialogue between (1) big data mining,
(2) qualitative studies and ground truth, and (3) theoretical modeling has been
found to be very productive in many fields of social network studies, especially for
modeling dynamic networks (Luo, 2011; Small, 2011; Creswell, 2013). While data
mining is useful for generating some preliminarily quantitative indicators or discovering some patterns regarding certain social phenomenon, mixed methods show
their utility through their strong ability to validate preliminary findings. Chapter
4 provides another example case to illustrate this. The authors try to uncover the
guanxi circle of a department leader in a Chinese organizational setting. A guanxi
circle, also like a Dunbar circle, has three layers of followers collected around an
egocentered network. This poses a research question: Which methods can be used

to quantitatively measure a leader’s guanxi circle? By collecting quantitative network data, the authors devised several computing methods to answer this question.