Abbe E (2017) Community detection and stochastic block models: recent developments
MATH
Google Scholar
Aiello LM, Barrat A, Cattuto C et al (2012) Link creation and information spreading over social and communication ties in an interest-based online social network. EPJ Data Sci 1(1):12
Article
Google Scholar
Alanis-Lobato G, Mier P, Andrade-Navarro M (2016) Manifold learning and maximum likelihood estimation for hyperbolic network embedding. Appl Netw Sci 1:10
Article
Google Scholar
Anania EC, Disher T, Anglin KM, Kring JP (2017) Selecting for long-duration space exploration: implications of personality. In 2017 IEEE Aerospace Conference. IEEE, Manhattan Beach, p 1–8
Anderson CJ, Wasserman S, Faust K (1992) Building stochastic blockmodels. Soc Networks 14:137–161. https://doi.org/10.1016/0378-8733(92)90017-2
Article
Google Scholar
Back MD (2015) Opening the process black box: mechanisms underlying the social consequences of personality. Eur J Personal 29(91):96. https://doi.org/10.1002/per.1999
Article
Google Scholar
Bang-Jensen J, Gutin GZ (2008) Digraphs: theory, algorithms and applications. Springer Science & Business Media, Springer-Verlag, London
Barabási AL (2003) Linked: how everything is connected to everything else and what it means. Basic Books a member of the Perseus Books Group, New York
Google Scholar
Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
Article
MathSciNet
Google Scholar
Barrenas F, Chavali S, Holme P, Mobini R, Benson M (2009) Network properties of complex human disease genes identified through genome-wide association studies. PLoS One 4(11):e8090
Article
Google Scholar
Bender EA, Canfield ER (1978) The asymptotic number of labeled graphs with given degree sequences. J Combinator Theory Ser A 24:296–307. https://doi.org/10.1016/0097-3165(78)90059-6
Article
MathSciNet
MATH
Google Scholar
Bernard HR, Killworth PD, Sailer L (1982) Informant accuracy in social-network data V. An experimental attempt to predict actual communication from recall data. Soc Sci Res 11:30–66. https://doi.org/10.1016/0049-089X(82)90006-0
Article
Google Scholar
Bickel PJ, Chen A (2009) A nonparametric view of network models and Newman-Girvan and other modularities. Proc Natl Acad Sci 106:21068–21073. https://doi.org/10.1073/pnas.0907096106
Article
MATH
Google Scholar
Bollobás B (1980) A probabilistic proof of an asymptotic formula for the number of labelled regular graphs. Eur J Comb 1:311–316. https://doi.org/10.1016/S0195-6698(80)80030-8
Article
MathSciNet
MATH
Google Scholar
Bollobás B (1998) Random graphs. In: Modern graph theory. Springer, New York, pp 215–252
Chapter
Google Scholar
Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Networks 29:555–564. https://doi.org/10.1016/j.socnet.2007.04.002
Article
Google Scholar
Borgatti SP, Everett MG, Freeman LC (2014) UCINET. In: Alhajj RRJ (ed) Encyclopedia of social network analysis and mining. Springer, New York
Google Scholar
Bouanan Y, Zacharewicz G, Ribault J, Vallespir B (2018) Discrete event system specification-based framework for modeling and simulation of propagation phenomena in social networks: application to the information spreading in a multi-layer social network, SIMULATION: Trans Soc Model Simul Int 1 2018. https://doi.org/10.1177/0037549718776368
Book
Google Scholar
Bradley JH, Hebert FJ (1997) The effect of personality type on team performance. J Manag Dev 16:337–353. https://doi.org/10.1108/02621719710174525
Article
Google Scholar
Bullington TS (2016) Followers that lead: relating leadership emergence through follower commitment, engagement, and connectedness. Conway, University of Central Arkansas. https://uca.edu/phdleadership/files/2012/07/Bullington-Followers-that-Lead-1.pdf
Capretz LF (2002) Is there an engineering type? World Trans Eng Technol Educ 1:169–172
Google Scholar
Catanese SA, De Meo P, Ferrara E et al (2011) Crawling facebook for social network analysis purposes. In: Proceedings of the international conference on web intelligence, mining and semantics. ACM, p 52
Chakrabarti D, Zhan Y, Faloutsos C (2004) R-MAT: a recursive model for graph mining. In: Proceedings of the 2004 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, p 442–446
Chen C (2007) Social networks at Sempra Energy’s IT division are key to building strategic capabilities. Glob Bus Organ Excell 26:16–24
Article
Google Scholar
Choo PK, Lou ZN, Camburn BA et al (2014) Ideation methods: a first study on measured outcomes with personality type. In: AASME 2014 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers, New York, p V007T07A019–V007T07A019
Chung F, Lu L (2002) The average distances in random graphs with given expected degrees. In: Proceedings of the National Academy of Sciences, 99(25). National Academy of Sciences of the United States of America, pp 15879–15882
Cohen Y, Ornoy H, Keren B (2013) MBTI personality types of project managers and their success: a field survey. Proj Manag J 44:78–87. https://doi.org/10.1002/pmj.21338
Article
Google Scholar
Crandall D, Cosley D, Huttenlocher D et al (2008) Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 160–168
Chapter
Google Scholar
Csárdi G, Nepusz T (2013) igraph Reference Manual, http://igraph.org/c/doc/igraph-docs.pdf
Google Scholar
Decelle A, Krzakala F, Moore C, Zdeborová L (2011) Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Phys Rev E 84:066106
Article
Google Scholar
DeChurch LA, Mesmer-Magnus JR, Center JS (2015) Maintaining shared mental models over long-duration exploration missions. NASA/TM-2015-218590. NASA, Houston
Google Scholar
Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. New York, Cambridge University Press
Emanuel RC (2013) Do certain personality types have a particular communication style. Int J Soc Sci Humanities 2:4–10
Google Scholar
Erdős, P., Rényi, A., On random graphs I. Publicationes Mathematicae Debrecen. Debrecen, Hungary, Institute of Mathematics, University of Debrecen. 6, pp. 290–297
Erdos P, Rényi A (1960) On the evolution of random graphs. Publ Math 5:17–61
MathSciNet
MATH
Google Scholar
Faust K, Wasserman S (1992) Blockmodels: interpretation and evaluation. Soc Networks 14:5–61
Article
Google Scholar
Felder RM, Brent R (2005) Understanding student differences. J Eng Educ 94:57–72
Article
Google Scholar
Felder RM, Felder GN, Dietz EJ (2002) The effects of personality type on engineering student performance and attitudes. J Eng Educ 91:3–17
Article
Google Scholar
Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174
Article
MathSciNet
Google Scholar
Frank O, Strauss D (1986) Markov graphs. J Am Stat Assoc 81:832–842
Article
MathSciNet
Google Scholar
Freeman B (2009) Personality type and medical specialty. University of Chicago Hospital, Chicago
Google Scholar
Freeman L (1988) Computer programs and social network analysis. Connections 11:26–31
Google Scholar
Freeman L (2016) (2008 September 21). Datasets. Department of Sociology and Institute for mathematical Behaviorial sciences, School of Social Sciences, University of California, Irvine, Retrieved September 9
Google Scholar
Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Networks 1:215–239
Article
Google Scholar
Furnham A, Crump J (2015a) Personality and management level: traits that differentiate leadership levels. Psychology 6:549
Article
Google Scholar
Furnham A, Crump J (2015b) The Myers-Briggs type Indicator (MBTI) and promotion at work. Psychology 6:1510–1515. https://doi.org/10.4236/psych.2015.612147
Article
Google Scholar
Gajewar A, Das Sarma A (2012) Multi-skill collaborative teams based on densest subgraphs. In: Proceedings of the 2012 SIAM international conference on data mining. SIAM, Philadelphia, p 165–176
Gersting JL (2014) Mathematical structures for computer science: discrete mathematics and its applications. W. H. Freeman and Company, New York
Google Scholar
Geyer CJ, Thompson EA (1992) Constrained Monte Carlo maximum likelihood for dependent data. J R Stat Soc Ser B Methodol 54(3):657-683
Gloor PA, Fischbach K, Fuehres H et al (2011) Towards “honest signals” of creativity – identifying personality characteristics through microscopic social network analysis. Procedia Soc Behav Sci 26:166–179. https://doi.org/10.1016/j.sbspro.2011.10.573
Article
Google Scholar
Goldberg LR (1990) An Alternative “Description of personality”: The Big-five factor structure. J Pers Soc Psychol 59:1216–1229. https://doi.org/10.1037/0022-3514.59.6.1216
Article
Google Scholar
Grandjean M (2016) A social network analysis of twitter: mapping the digital humanities community. Cogent Arts Human 3:14. https://doi.org/10.1080/23311983.2016.1171458
Article
Google Scholar
Grant A (2013) Goodbye to MBTI: the fad that won’t die. Psychology Today
Holland PW, Laskey KB, Leinhardt S (1983) Stochastic blockmodels: first steps. Soc Networks 5:109–137
Article
MathSciNet
Google Scholar
Holland PW, Leinhardt S (1977) A method for detecting structure in sociometric data. In Social Networks (pp. 411-432). Academic Press. Retrieved from https://www.elsevier.com/books/social-networks/leinhardt/978-0-12-442450-0
Holland PW, Leinhardt S (1981) An exponential family of probability distributions for directed graphs. J Am Stat Assoc 76:33–50. https://doi.org/10.1080/01621459.1981.10477598
Article
MathSciNet
MATH
Google Scholar
Hunter DR (2007) Curved exponential family models for social networks. Soc Networks 29:216–230
Article
Google Scholar
Jafrani S, Zehra N, Zehra M et al (2017) Assessment of personality type and medical specialty choice among medical students from Karachi; using Myers-Briggs type Indicator (MBTI) tool. J Pak Med Assoc 67:520–526
Google Scholar
John OP, Srivastava S (1999) The big five trait taxonomy: history, measurement, and theoretical perspectives. In: Handbook of personality: Theory and research, vol 2, pp 102–138
Google Scholar
Jung CG (1971) Psychological types. In: Volume 6 of the collected works of CG Jung. Princeton University Press, Princeton, p 169–170
Keirsey D (1998) Please Understand Me II. Prometheus Nemesis Book Company, P.O. Box 2748 Del Mar, California 92014
Google Scholar
Kiss M, Kun A, Kapitány A, Erdei P (2014) Regression Analysis of the Effect of Personality-Career Match on the Academic Performance in Business Higher Education: An Evidence from the University of Debrecen (March 22, 2014). Tudás – Tanulás – Szabadság Neveléstudományi Konferencia, Cluj-Napoca, pp 223–227
Google Scholar
Knoke D, Yang S (2008) Social network analysis, Second. SAGE Publications, Thousand Oaks
Krackhardt D (1987) Cognitive social structures. Soc Networks 9:109–134
Article
MathSciNet
Google Scholar
Krebs V (2008) Social capital: the key to success for the 21st century organization. IHRIM J 12:38–42
Google Scholar
Kwak H, Lee C, Park H, Moon S (2010) What is Twitte ra social network or a news media? In: Proceedings of the 19th international conference on world wide web. ACM, New York, pp 591–600
Chapter
Google Scholar
Landon LB, Vessey WB, Barrett JD (2015) Risk of performance and behavioral health decrements due to inadequate cooperation coordination, communication, and psychosocial adaptation within a team (JSC-CN-34195). NASA Conf Publ
Lazega E (2001) The collegial phenomenon: the social mechanisms of cooperation among peers in a corporate law partnership. Oxford New York, Oxford University Press, on Demand
Leskovec J, Chakrabarti D, Kleinberg J et al (2010) Kronecker graphs: an approach to modeling networks. J Mach Learn Res 11:985–1042
MathSciNet
MATH
Google Scholar
Li Y, Cao H, Wen G (2018) Simulation study on opinion formation models of heterogenous agents based on game theory and complex networks. SIMULATION 93(11):899–919
Article
Google Scholar
Loffredo DA, Opt SK, Harrington R (2008) Communicator style and MBTI extraversion-introversion domains. J Psychol Type 68:29–36
Google Scholar
Mahadevan P, Krioukov D, Fall K, Vahdat A (2006) Systematic topology analysis and generation using degree correlations. In: SIGCOMM A (ed) Proceedings of the 2006 conference on applications, technologies, architectures, and protocols for computer communications. ACM, New York, pp 135–146
Google Scholar
Malik M, Zamir S (2014) The relationship between Myers Briggs type Indicator (MBTI) and emotional intelligence among university students. J Educ Pract 5:35–42
Google Scholar
Manso B, Manso M (2010) Know the network, knit the network: applying SNA to N2C2 maturity model experiments. EDISOFT SA MONTE CAPARICA (PORTUGAL) http://www.dtic.mil/dtic/tr/fulltext/u2/a546862.pdf
Marioles, N. S., Strickert, D. P., & Hammer, A. L. (1996). Attraction, satisfaction, and psychological types of couples. Journal of Psychological Type, 36, 16–27.
Google Scholar
McCaulley MH (1977) Application of the Myers-Briggs type indicator to medicine and other health professions. Center for Applications of Psychological Type, Gainesville
Google Scholar
McCrae RR, Costa PT (1987) Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol 52:81. https://doi.org/10.1037/0022-3514.52.1.81
Article
Google Scholar
McCrae RR, Costa PT (1989) Reinterpreting the Myers-Briggs type indicator from the perspective of the five-factor model of personality. J Pers 57:17–40. https://doi.org/10.1111/j.1467-6494.1989.tb00759.x
Article
Google Scholar
Metzner R, Burney C, Mahlberg A (1981) Towards a reformulation of the typology of functions. J Anal Psychol 26:33–47. https://doi.org/10.1111/j.1465-5922.1981.00033.x
Article
Google Scholar
Milo R, Kashtan N, Itzkovitz S, et al (2003) On the uniform generation of random graphs with prescribed degree sequences. cond-mat/0312028
Google Scholar
Mislove A, Marcon M, Gummadi KP et al (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement. ACM, New York, pp 29–42
Chapter
Google Scholar
Mitchell WD (1996) The distribution of MBTI types in the US by gender and ethnic group. J Psychol Type 37:3
Google Scholar
Molloy M, Reed B (1995) A critical point for random graphs with a given degree sequence. Random Struct Algoritm 6:161–180. https://doi.org/10.1002/rsa.3240060204
Article
MathSciNet
MATH
Google Scholar
Molloy M, Reed B (1998) The size of the giant component of a random graph with a given degree sequence. Comb Probab Comput 7:295–305
Article
MathSciNet
Google Scholar
Moutafi J, Furnham A, Crump J (2007) Is managerial level related to personality? Br J Manag 18:272–280. https://doi.org/10.1111/j.1467-8551.2007.00511.x
Article
Google Scholar
Myers IB (1962) The Myers-Briggs type indicator: manual. Consulting Psychologists Press, Palo Alto
Book
Google Scholar
Myers IB, McCauley MH (1985) Manual: a guide to the development and use of the Myers-Briggs type Indicator. Consulting Psychologists Press, Palo Alto, California
Google Scholar
Narayanan A, Shi E, Rubinstein BI (2011) Link prediction by de-anonymization: how we won the kaggle social network challenge. In: The 2011 international joint conference on neural networks conference proceedings. IEEE Computational intelligence society, Piscataway, pp 1825–1834
Chapter
Google Scholar
Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In: 2008 IEEE symposium on security and privacy. IEEE Computer Society, Los Alamitos, pp 111–125
Chapter
Google Scholar
Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In: 2009 30th IEEE symposium on security and privacy. IEEE computer society conference publishing services, Los Alamitos, pp 173–187
Chapter
Google Scholar
Nelson J, Bolton J (2008) Systems engineering behavior and leadership study. Johnson Space Center, National Aeronautics and Space Administration, Houston
Newman M (2010) Networks: an introduction. Oxford University Press, New York
Book
Google Scholar
Newman ME (2003) The structure and function of complex networks. SIAM Rev 45:167–256. https://doi.org/10.1137/S003614450342480
Article
MathSciNet
MATH
Google Scholar
Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113. https://doi.org/10.1103/PhysRevE.69.026113
Article
Google Scholar
Newman ME, Strogatz SH, Watts DJ (2001) Random graphs with arbitrary degree distributions and their applications. Phys Rev E 64. https://doi.org/10.1103/PhysRevE.64.026118
Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96:1077–1087. https://doi.org/10.1198/016214501753208735
Article
MathSciNet
MATH
Google Scholar
Papadopoulos F, Kitsak M, Serrano MÁ et al (2012) Popularity versus similarity in growing networks. Nature 489:537
Article
Google Scholar
Pattison P, Wasserman S, Robins G, Kanfer AM (2000) Statistical evaluation of algebraic constraints for social networks. J Math Psychol 44:536–568. https://doi.org/10.1006/jmps.1999.1261
Article
MathSciNet
MATH
Google Scholar
R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna URL https://www.R-project.org/
Google Scholar
Rapoport A (1957) Contribution to the theory of random and biased nets. Bull Math Biophys 19:257–277. https://doi.org/10.1007/BF02478417
Article
MathSciNet
Google Scholar
Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Soc Networks 29:173–191. https://doi.org/10.1016/j.socnet.2006.08.002
Article
Google Scholar
Roethlisberger FJ, Dickson WJ (1939) Management and the worker. Harvard University Press, Cambridge
Google Scholar
Rosati P (1993) Student retention from first-year engineering related to personality type. In: Frontiers in education conference, 1993. Twenty-third annual conference. “Engineering education: renewing America’s technology”, proceedings. IEEE, Piscataway, pp 37–39
Google Scholar
Rushton JP, Irwing P (2008) A general factor of personality (GFP) from two meta-analyses of the big five: Digman (1997) and mount, Barrick, Scullen, and rounds (2005). Personal Individ Differ 45:679–683. https://doi.org/10.1016/j.paid.2008.07.015
Article
Google Scholar
Sampson S (1969) Crisis in a cloister. Unpublished doctoral dissertation. Cornell University. https://www.uni-due.de/hummell/netzwerkbuch/ucinet/prog/UCI%20IV-%20Einzeldateien/uci4_dat.pdf
Schwimmer E (1973) Exchange in the social structure of the Orokaiva: traditional and emergent ideologies in the Northern District of Papua. London, Hurst and Co
Schwimmer E (1979) Reciprocity and structure: a semiotic analysis of some Orokaiva exchange data. Man 14:271–285. https://doi.org/10.2307/2801567
Article
Google Scholar
Scott J (2000) Social network analysis: a handbook, second. SAGE publications, Inc, Thousand Oaks
Google Scholar
Scott J, Carrington PJ (2011) The SAGE handbook of social network analysis. SAGE publications, Inc, Thousand Oaks
Google Scholar
Seshadhri C, Kolda TG, Pinar A (2012) Community structure and scale-free collections of Erdős-Rényi graphs. Physical Rev E 85. https://doi.org/10.1103/PhysRevE.85.056109
Smathers (2003) (Guide to the Isabel Briggs Myers Papers 1885–1992). University of Florida George A. Smathers Libraries, Department of Special and Area Studies Collections, Gainesville, FL. 2003. http://web.uflib.ufl.edu/spec/manuscript/guides/Myers.htm Retrieved February 28
Snijders TA (2002) Markov chain Monte Carlo estimation of exponential random graph models. J Soc Struct 3:1–40
Google Scholar
Staudt CL, Hamann M, Gutfraind A et al (2017) Generating realistic scaled complex networks. Appl Netw Sci 2:36. https://doi.org/10.1007/s41109-017-0054-z
Article
Google Scholar
Strogatz SH (2001) Exploring complex networks. Nature 410:268–276. https://doi.org/10.1038/35065725
Article
MATH
Google Scholar
Thurman B (1979) In the office: networks and coalitions. Soc Networks 2:47–63. https://doi.org/10.1016/0378-8733(79)90010-8
Article
Google Scholar
Tsvetovat M, Carley K (2005) Generation of realistic social network datasets for testing of analysis and simulation tools. Carnegie Mellon University. Available at SSRN 2729296, Elsevier, Amsterdam
Tupes EC, Christal RE (1992) Recurrent personality factors based on trait ratings. J Pers 60:225–251. https://doi.org/10.1111/j.1467-6494.1992.tb00973.x
Article
Google Scholar
van Mierlo T, Hyatt D, Ching AT (2016) Employing the Gini coefficient to measure participation inequality in treatment-focused digital health social networks. Netw Model Anal Health Inform Bioinform 5:32
Article
Google Scholar
Viger F, Latapy M (2005) Efficient and simple generation of random simple connected graphs with prescribed degree sequence. In: International computing and combinatorics conference. Springer, Berlin Heidelberg, pp 440–449
Chapter
Google Scholar
Wasserman S, Pattison P (1996) Logit models and logistic regressions for social networks: I an introduction to Markov graphs and p*. Psychometrika 61:401–425
Article
MathSciNet
Google Scholar
Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440. https://doi.org/10.1038/30918
Article
MATH
Google Scholar
Webster CM (1993) Task-related and context-based constraints in observed and reported relational data. PhD Thesis. University of California, Irvine
Google Scholar
Weiler DT (2017) The effect of role assignment and personality subtypes in simulation on critical thinking development, situation awareness, and perceived self-efficacy of nursing baccalaureate students. Master’s Thesis. University of Louisville
Yang J, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42:181–213. https://doi.org/10.1007/s10115-013-0693-z
Article
Google Scholar
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473. https://doi.org/10.1086/jar.33.4.3629752
Article
Google Scholar
Zhou B, Pei J, Luk W (2008) A brief survey on anonymization techniques for privacy preserving publishing of social network data. SIGKDD explorations 10:12–22. https://doi.org/10.1145/1540276.1540279
Article
Google Scholar