biblio.bib

@article{brandenberger2018trading,
  title = {Trading Favors\textemdash{{Examining}} the Temporal Dynamics of Reciprocity in Congressional Collaborations Using Relational Event Models},
  author = {Brandenberger, Laurence},
  year = {2018},
  month = jul,
  volume = {54},
  pages = {238--253},
  issn = {0378-8733},
  doi = {10.1016/j.socnet.2018.02.001},
  journal = {Social Networks}
}


@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@Book{Carolan2014-dn,
  title     = "Social network analysis and education: Theory, methods \&
               applications",
  author    = "Carolan, Brian V",
  publisher = "SAGE Publications",
  year      =  2014,
  address   = "Thousand Oaks, CA"
}
@ARTICLE{Kennedy2016-qw,
  title     = "Open Annotation and Close Reading the Victorian Text: Using
               Hypothes.is with Students",
  author    = "Kennedy, Meegan",
  journal   = "Journal of Victorian Culture",
  publisher = "Taylor \& Francis",
  volume    =  0,
  number    =  0,
  pages     = "1--9",
  year      =  2016,
  eprint    = "http://dx.doi.org/10.1080/13555502.2016.1233905",
  doi       = "10.1080/13555502.2016.1233905"
}

@BOOK{Scott2011-my,
  title     = "The {SAGE} Handbook of Social Network Analysis",
  author    = "Scott, J and Carrington, P J",
  publisher = "SAGE Publications",
  series    = "The Sage Handbook",
  year      =  2011,
  isbn      = "9781847873958",
  lccn      = "2010935654"
}

@article{grunspan2014understanding,
  title = {Understanding {{Classrooms}} through {{Social Network Analysis}}: {{A Primer}} for {{Social Network Analysis}} in {{Education Research}}},
  shorttitle = {Understanding {{Classrooms}} through {{Social Network Analysis}}},
  author = {Grunspan, Daniel Z. and Wiggins, Benjamin L. and Goodreau, Steven M.},
  editor = {Dolan, Erin},
  year = {2014},
  month = jun,
  volume = {13},
  pages = {167--178},
  issn = {1931-7913},
  doi = {10.1187/cbe.13-08-0162},
  journal = {CBE\textemdash Life Sciences Education},
  number = {2}
}

@BOOK{Scott2013-wu,
  title     = "Social network analysis",
  author    = "Scott, John",
  publisher = "SAGE Publications",
  edition   =  3,
  year      =  2013,
  address   = "Thousand Oaks, CA"
}
@ARTICLE{Borgatti2009-du,
  title    = "Network analysis in the social sciences",
  author   = "Borgatti, Stephen P and Mehra, Ajay and Brass, Daniel J and
              Labianca, Giuseppe",
  abstract = "Over the past decade, there has been an explosion of interest in
              network research across the physical and social sciences. For
              social scientists, the theory of networks has been a gold mine,
              yielding explanations for social phenomena in a wide variety of
              disciplines from psychology to economics. Here, we review the
              kinds of things that social scientists have tried to explain
              using social network analysis and provide a nutshell description
              of the basic assumptions, goals, and explanatory mechanisms
              prevalent in the field. We hope to contribute to a dialogue among
              researchers from across the physical and social sciences who
              share a common interest in understanding the antecedents and
              consequences of network phenomena.",
  journal  = "Science",
  volume   =  323,
  number   =  5916,
  pages    = "892--895",
  month    =  feb,
  year     =  2009,
  keywords = "Behavioral Research; Community Networks; Humans; Interpersonal
              Relations; Psychological Theory; Social Sciences; Social
              Sciences: trends; Social Support",
  issn     = "0036-8075, 1095-9203",
  pmid     = "19213908",
  doi      = "10.1126/science.1165821"
}

@INCOLLECTION{Niglas2010-tr,
  title     = "The Multidimensional Model of Research Methodology: An
               Integrated Set of Continua",
  booktitle = "{SAGE} Handbook of Mixed Methods in Social \& Behavioral
               Research",
  author    = "Niglas, Katrin",
  editor    = "Tashakkori, Abbas and Teddlie, Charles",
  publisher = "SAGE",
  pages     = "215--236",
  month     =  "21~" # jun,
  year      =  2010,
  address   = "Thousand Oaks",
  isbn      = "9781412972666"
}

@ARTICLE{Zhao2016-th,
  title         = "Network Inference from Grouped Data",
  author        = "Zhao, Yunpeng and Weko, Charles",
  abstract      = "In medical research, economics, and the social sciences data
                   frequently appear as subsets of a set of objects. Over the
                   past century a number of descriptive statistics have been
                   developed to construct network structure from such data.
                   However, these measures lack a generating mechanism that
                   links the inferred network structure to the observed groups.
                   To address this issue, we propose a model-based approach
                   called the Hub Model which assumes that every observed group
                   has a leader and that the leader has brought together the
                   other members of the group. The performance of Hub Models is
                   demonstrated by simulation studies. We apply this model to
                   infer the relationships among Senators serving in the 110th
                   United States Congress, the characters in a famous 18th
                   century Chinese novel, and the distribution of flora in
                   North America.",
  month         =  "11~" # sep,
  year          =  2016,
  archivePrefix = "arXiv",
  eprint        = "1609.03211",
  primaryClass  = "cs.SI",
  arxivid       = "1609.03211"
}

@BOOK{Scott2012-fq,
  title     = "Social Network Analysis",
  author    = "Scott, John",
  abstract  = "The Third Edition of this best-selling text has been fully
               revised and updated to include coverage of the many developments
               on social network analysis (SNA) over the last decade. Written
               in a clear and accessible style, the book introduces these
               topics to newcomers and non-specialists and gives sufficient
               detail for more advanced users of social network analysis.
               Throughout the book, key ideas are discussed in relation to the
               principal software programs available for SNA. The book provides
               a comprehensive overview of the field, outlining both its
               theoretical basis and its key techniques. Drawing from the core
               ideas of points, lines and paths, John Scott builds a framework
               of network analysis that covers such measures as density,
               centrality, clustering, centralisation, and spatialisation. He
               identifies the various types of clique, component, and circle
               into which networks are formed, and he outlines an approach to
               socially structured positions within networks. A completely new
               chapter in this edition discusses recent work on network
               dynamics and methods for studying change over time. A final
               chapter discusses approaches to network visualisation. This is
               an excellent resource for researchers across the social sciences
               and for students of social theory and research methods.",
  publisher = "SAGE",
  month     =  "19~" # nov,
  year      =  2012,
  language  = "en",
  isbn      = "9781446259450",
  doi       = "10.5040/9781849668187"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Rivers2014-wt,
  title    = "Ethical research standards in a world of big data",
  author   = "Rivers, Caitlin M and Lewis, Bryan L",
  abstract = "In 2009 Ginsberg et al. reported using Google search query volume
              to estimate influenza activity in advance of traditional
              methodologies. It was a groundbreaking example of digital disease
              detection, and it still remains illustrative of the power of
              gathering data from the internet for important research. In
              recent years, the methodologies have been extended to include new
              topics and data sources; Twitter in particular has been used for
              surveillance of influenza-like-illnesses, political sentiments,
              and even behavioral risk factors like sentiments about childhood
              vaccination programs. As the research landscape continuously
              changes, the protection of human subjects in online research
              needs to keep pace. Here we propose a number of guidelines for
              ensuring that the work done by digital researchers is supported
              by ethical-use principles. Our proposed guidelines include: 1)
              Study designs using Twitter-derived data should be transparent
              and readily available to the public. 2) The context in which a
              tweet is sent should be respected by researchers. 3) All data
              that could be used to identify tweet authors, including
              geolocations, should be secured. 4) No information collected from
              Twitter should be used to procure more data about tweet authors
              from other sources. 5) Study designs that require data collection
              from a few individuals rather than aggregate analysis require
              Institutional Review Board (IRB) approval. 6) Researchers should
              adhere to a user’s attempt to control his or her data by
              respecting privacy settings. As researchers, we believe that a
              discourse within the research community is needed to ensure
              protection of research subjects. These guidelines are offered to
              help start this discourse and to lay the foundations for the
              ethical use of Twitter data.",
  journal  = "F1000Research",
  volume   =  3,
  month    =  "21~" # aug,
  year     =  2014,
  doi      = "10.12688/f1000research.3-38.v2"
}


@ARTICLE{Kraut2004-se,
  title       = "Psychological research online: report of Board of Scientific
                 Affairs' Advisory Group on the Conduct of Research on the
                 Internet",
  author      = "Kraut, Robert and Olson, Judith and Banaji, Mahzarin and
                 Bruckman, Amy and Cohen, Jeffrey and Couper, Mick",
  affiliation = "Human-Computer Interaction Institute, Carnegie Mellon
                 University, Pittsburgh, PA, USA. robert.kraut@cmu.edu",
  abstract    = "As the Internet has changed communication, commerce, and the
                 distribution of information, so too it is changing
                 psychological research. Psychologists can observe new or rare
                 phenomena online and can do research on traditional
                 psychological topics more efficiently, enabling them to expand
                 the scale and scope of their research. Yet these opportunities
                 entail risk both to research quality and to human subjects.
                 Internet research is inherently no more risky than traditional
                 observational, survey, or experimental methods. Yet the risks
                 and safeguards against them will differ from those
                 characterizing traditional research and will themselves change
                 over time. This article describes some benefits and challenges
                 of conducting psychological research via the Internet and
                 offers recommendations to both researchers and institutional
                 review boards for dealing with them. ((c) 2004 APA, all rights
                 reserved)",
  journal     = "The American psychologist",
  volume      =  59,
  number      =  2,
  pages       = "105--117",
  month       =  feb,
  year        =  2004,
  language    = "en",
  issn        = "0003-066X",
  pmid        = "14992637",
  doi         = "10.1037/0003-066X.59.2.105"
}


@ARTICLE{Wickham2014-jr,
  title    = "Tidy Data",
  author   = "Wickham, Hadley",
  abstract = "A huge amount of effort is spent cleaning data to get it ready
              for analysis, but there has been little research on how to make
              data cleaning as easy and effective as possible. This paper
              tackles a small, but important, component of data cleaning: data
              tidying. Tidy datasets are easy to manipulate, model and
              visualize, and have a specific structure: each variable is a
              column, each observation is a row, and each type of observational
              unit is a table. This framework makes it easy to tidy messy
              datasets because only a small set of tools are needed to deal
              with a wide range of un-tidy datasets. This structure also makes
              it easier to develop tidy tools for data analysis, tools that
              both input and output tidy datasets. The advantages of a
              consistent data structure and matching tools are demonstrated
              with a case study free from mundane data manipulation chores.",
  journal  = "Journal of statistical software",
  volume   =  59,
  number   =  1,
  pages    = "1--23",
  year     =  2014,
  issn     = "1548-7660",
  doi      = "10.18637/jss.v059.i10"
}
@ARTICLE{Dawson2010-se,
  title   = "‘Seeing’ the learning community: An exploration of the development
             of a resource for monitoring online student networking",
  author  = "Dawson, Shane",
  journal = "British journal of educational technology: journal of the Council
             for Educational Technology",
  volume  =  41,
  number  =  5,
  pages   = "736--752",
  month   =  sep,
  year    =  2010,
  issn    = "0007-1013",
  doi     = "10.1111/j.1467-8535.2009.00970.x"
}
@BOOK{Burnham2003-eo,
  title     = "Model selection and multimodel inference: a practical
               information-theoretic approach",
  author    = "Burnham, Kenneth P and Anderson, David R",
  publisher = "Springer Science \& Business Media",
  year      =  2003
}
@BOOK{Apkarian2016-td,
  title     = "Statistical Analysis of Social Networks",
  author    = "Apkarian, Jacob and Hanneman, Robert A",
  publisher = "City University of New York, York College",
  year      =  2016,
  address   = "Jamaica, NY"
}
@BOOK{Kolaczyk2014-qj,
  title     = "Statistical Analysis of Network Data with R:",
  author    = "Kolaczyk, Eric D and Cs{\'a}rdi, G{\'a}bor",
  publisher = "Springer New York",
  series    = "Use R!",
  year      =  2014,
  isbn      = "9781493909827, 9781493909834",
  doi       = "10.1007/978-1-4939-0983-4"
}
@ARTICLE{Snijders2011-uu,
  title    = "Statistical Models for Social Networks",
  author   = "Snijders, Tom A B",
  abstract = "Statistical models for social networks as dependent variables
              must represent the typical network dependencies between tie
              variables such as reciprocity, homophily, transitivity, etc. This
              review first treats models for single (cross-sectionally
              observed) networks and then for network dynamics. For single
              networks, the older literature concentrated on conditionally
              uniform models. Various types of latent space models have been
              developed: for discrete, general metric, ultrametric, Euclidean,
              and partially ordered spaces. Exponential random graph models
              were proposed long ago but now are applied more and more thanks
              to the non-Markovian social circuit specifications that were
              recently proposed. Modeling network dynamics is less complicated
              than modeling single network observations because dependencies
              are spread out in time. For modeling network dynamics,
              continuous-time models are more fruitful. Actor-oriented models
              here provide a model that can represent many dependencies in a
              flexible way. Strong model development is now going on to combine
              the features of these models and to extend them to more
              complicated outcome spaces.",
  journal  = "Annual review of sociology",
  volume   =  37,
  number   =  1,
  pages    = "131--153",
  year     =  2011,
  eprint   = "http://dx.doi.org/10.1146/annurev.soc.012809.102709",
  issn     = "0360-0572",
  doi      = "10.1146/annurev.soc.012809.102709"
}

@INPROCEEDINGS{Gloor2004-vy,
  title       = "Temporal visualization and analysis of social networks",
  booktitle   = "{NAACSOS} Conference, June",
  author      = "Gloor, P and Laubacher, Rob and Zhao, Yan and Dynes, S",
  pages       = "27--29",
  institution = "Citeseer",
  year        =  2004
}

@ARTICLE{Rodriguez2014-kx,
  title     = "Uncovering the structure and temporal dynamics of information
               propagation",
  author    = "Rodriguez, Manuel Gomez and Leskovec, Jure and Balduzzi, David
               and Sch{\"o}lkopf, Bernhard",
  abstract  = "Uncovering the structure and temporal dynamics of information
               propagation - Volume 2 Issue 1 - MANUEL GOMEZ RODRIGUEZ, JURE
               LESKOVEC, DAVID BALDUZZI, BERNHARD SCH{\"O}LKOPF",
  journal   = "Network Science",
  publisher = "Cambridge University Press",
  volume    =  2,
  number    =  1,
  pages     = "26--65",
  month     =  apr,
  year      =  2014,
  keywords  = "diffusion networks; information cascades; information
               propagation; meme tracking; information networks; social
               networks; news media; blogs",
  issn      = "2050-1242, 2050-1250",
  doi       = "10.1017/nws.2014.3"
}

@ARTICLE{Raghavan2014-uc,
  title    = "Modeling Temporal Activity Patterns in Dynamic Social Networks",
  author   = "Raghavan, V and Steeg, G Ver and Galstyan, A and Tartakovsky, A G",
  abstract = "The focus of this work is on developing probabilistic models for
              temporal activity of users in social networks (e.g., posting and
              tweeting) by incorporating the social network influence as
              perceived by the user. Although prior work in this area has
              developed sophisticated models for user activity, these models
              either ignore social network influence completely or incorporate
              it in an implicit manner. We overcome the nontransparency of the
              network in the model at the individual scale by proposing a
              coupled hidden Markov model (HMM), where each user's activity
              evolves according to a Markov chain with a hidden state that is
              influenced by the collective activity of the friends of the user.
              We develop generalized Baum-Welch and Viterbi algorithms for
              parameter learning and state estimation for the proposed
              framework. We then validate the proposed model using a
              significant corpus of user activity on Twitter. Our numerical
              studies show that with sufficient observations to ensure accurate
              model learning, the proposed framework explains the observed data
              better than either a renewal process-based model or a
              conventional (uncoupled) HMM. We also demonstrate the utility of
              the proposed approach in predicting the time to the next tweet.
              Finally, clustering in the model parameter space is shown to
              result in distinct natural clusters of users characterized by the
              interaction dynamic between a user and his network.",
  journal  = "IEEE Transactions on Computational Social Systems",
  volume   =  1,
  number   =  1,
  pages    = "89--107",
  month    =  mar,
  year     =  2014,
  keywords = "hidden Markov models;learning (artificial intelligence);pattern
              clustering;social networking (online);Baum-Welch
              algorithms;HMM;Markov chain;Twitter;Viterbi algorithms;dynamic
              social networks;hidden Markov model;model parameter space
              clustering;parameter learning;probabilistic models;renewal
              process-based model;social network influence;state
              estimation;temporal activity pattern modeling;user
              activity;Biological system modeling;Computational modeling;Data
              models;Hidden Markov models;Mathematical model;Numerical
              models;Social network services;Activity profile
              modeling;Twitter;coupled hidden Markov model (coupled HMM);data
              fitting;explanation;hidden Markov model (HMM);prediction;social
              network influence;user clustering",
  issn     = "2329-924X",
  doi      = "10.1109/TCSS.2014.2307453"
}

@ARTICLE{Jiang2013-ew,
  title    = "Mining the Temporal Evolution of the Android Bug Reporting
              Community via Sliding Windows",
  author   = "Jiang, Feng and Wang, Jiemin and Hindle, Abram and Nascimento,
              Mario A",
  abstract = "The open source development community consists of both paid and
              volunteer developers as well as new and experienced users.
              Previous work has applied social network analysis (SNA) to open
              source communities and has demonstrated value in expertise
              discovery and triaging. One problem with applying SNA directly to
              the data of the entire project lifetime is that the impact of
              local activities will be drowned out. In this paper we provide a
              method for aggregating, analyzing, and visualizing local (small
              time periods) interactions of bug reporting participants by using
              the SNA to measure the betweeness centrality of these
              participants. In particular we mined the Android bug repository
              by producing social networks from overlapping 30-day windows of
              bug reports, each sliding over by day. In this paper we define
              three patterns of participant behaviour based on their local
              centrality. We propose a method of analyzing the centrality of
              bug report participants both locally and globally, then we
              conduct a thorough case study of the bug reporter's activity
              within the Android bug repository. Furthermore, we validate the
              conclusions of our method by mining the Android version control
              system and inspecting the Android release history. We found that
              windowed SNA analysis elicited local behaviour that were
              invisible during global analysis.",
  journal  = "CoRR",
  volume   = "abs/1310.7",
  month    =  "28~" # oct,
  year     =  2013,
  arxivid  = "1310.7469"
}

@ARTICLE{Carley2014-xa,
  title     = "Embassies burning: toward a near-real-time assessment of social
               media using geo-temporal dynamic network analytics",
  author    = "Carley, Kathleen M and Pfeffer, J{\"u}rgen and Morstatter, Fred
               and Liu, Huan",
  abstract  = "Effective crisis response requires rapid assessment of a
               situation in order to form actionable plans. Social media and
               traditional media are critical to this assessment. This paper
               describes a rapid ethnographic approach for extracting
               information from Twitter and news media and then assessing that
               information using dynamic network analysis techniques. Text
               mining high-dimensional network analytics and visualization are
               combined to provide an integrated approach to assessing large
               dynamic networks. This approach was used as the Benghazi
               consulate and the Egyptian embassy were attacked in 2012. This
               near-real-time assessment was set against a backdrop of ongoing
               data collection associated with the Arab Spring countries. This
               ongoing collection provided a baseline for Libya and Egypt
               against which the new data could be assessed. Herein, the
               outcome of that near-real-time assessment, the tools used, the
               lessons learned, and the results discovered are described. The
               same approach was used in other crisis events including
               SuperStorm Sandy, the Kenyan elections, from which examples are
               also drawn. We find that to be effective such analytics require
               the use of multiple media, deep dives into specific secondary
               issues, and a high-level assessment of not just who is doing
               what, but who is providing what information. Finally, we show
               the criticality of baseline data for interpreting the behavior
               during a crisis.",
  journal   = "Social Network Analysis and Mining",
  publisher = "Springer Vienna",
  volume    =  4,
  number    =  1,
  pages     = "195",
  month     =  "1~" # dec,
  year      =  2014,
  language  = "en",
  issn      = "1869-5450, 1869-5469",
  doi       = "10.1007/s13278-014-0195-3"
}

@ARTICLE{Myllari2010-ad,
  title    = "The dynamics of an online knowledge building community: A 5-year
              longitudinal study",
  author   = "Myllari, J and Ahlberg, M and Dillon, P",
  abstract = "This paper reports a 5-year design experiment on cumulative
              knowledge building as part of an international project. Through a
              longitudinal study and analysis of cumulative research data, we
              sought to answer the question, 'what happened and why in
              knowledge building?' Research data constitute messages which
              participants have written into a shared knowledge building
              database. A multi-method approach combing quantitative and
              qualitative data was adopted which integrated analysis of message
              generation, content analysis, network analysis, structure of
              message threads, discourse analysis and interviews. Conclusions
              are based on analysis of almost 2000 messages. Qualitative
              content analysis reveals 14 main categories of data. When the
              content of the messages are analysed, quantitatively cumulative
              trends emerge. When the frequencies of messages are plotted
              against time, peaks and troughs of message writing are revealed.
              The explanations for these patterns and variations are sought
              through interviews. Social network analysis shows that the
              network is centralised. The research literature suggests that
              decentralised networks are ideal, but in this particular case,
              the expert centralisation was beneficial for knowledge building
              in the collaborative and associated professional networks. The
              reasons for this are discussed.",
  journal  = "British journal of educational technology: journal of the Council
              for Educational Technology",
  volume   =  41,
  number   =  3,
  pages    = "365--387",
  year     =  2010,
  issn     = "0007-1013",
  doi      = "10.1111/j.1467-8535.2009.00972.x"
}