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Journal articles on the topic 'Social network analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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1

Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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2

Quinn, Darren, Liming Chen, and Maurice Mulvenna. "Social Network Analysis." International Journal of Ambient Computing and Intelligence 4, no. 3 (July 2012): 46–58. http://dx.doi.org/10.4018/jaci.2012070104.

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Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.

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3

Rowley, Timothy J. "Social Network Analysis." Proceedings of the International Association for Business and Society 7 (1996): 999–1009. http://dx.doi.org/10.5840/iabsproc1996794.

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4

S., Sukharev O., and Kurmanov N.V. "Social Network Analysis." Advances in Economics and Business 2, no. 3 (March 2014): 121–26. http://dx.doi.org/10.13189/aeb.2014.020301.

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Jain, Susha, Mahaveer Jain, and Balasubramani R. "Social Network Analysis." IJARCCE 8, no. 5 (May 30, 2019): 236–40. http://dx.doi.org/10.17148/ijarcce.2019.8543.

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6

Madani, Youness, Mohammed Erritali, Jamaa Bengourram, and Francoise Sailhan. "Social Network Analysis." Journal of Information Technology Research 13, no. 3 (July 2020): 142–55. http://dx.doi.org/10.4018/jitr.2020070109.

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Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.

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7

Scott, John. "Social Network Analysis." Sociology 22, no. 1 (February 1988): 109–27. http://dx.doi.org/10.1177/0038038588022001007.

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Streeter, Calvin L., and David F. Gillespie. "Social Network Analysis." Journal of Social Service Research 16, no. 1-2 (March 24, 1993): 201–22. http://dx.doi.org/10.1300/j079v16n01_10.

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9

Buštíková, Lenka. "Social Network Analysis." Czech Sociological Review 35, no. 2 (April 1, 1999): 193–206. http://dx.doi.org/10.13060/00380288.1999.35.2.10.

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10

Comunian, Roberta. "Social network analysis." Regional Insights 2, no. 2 (September 2011): 3. http://dx.doi.org/10.1080/20429843.2011.9727917.

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Sweeney, Patricia M., Elizabeth F. Bjerke, Hasan Guclu, Christopher R. Keane, Jared Galvan, Sherrianne M. Gleason, and Margaret A. Potter. "Social Network Analysis." Journal of Public Health Management and Practice 19, no. 6 (2013): E38—E40. http://dx.doi.org/10.1097/phh.0b013e31829fc013.

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12

GALASKIEWICZ, JOSEPH, and STANLEY WASSERMAN. "Social Network Analysis." Sociological Methods & Research 22, no. 1 (August 1993): 3–22. http://dx.doi.org/10.1177/0049124193022001001.

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13

Almahmoud, Essam, and Hemanta Kumar Doloi. "Assessment of Social Sustainability in Construction Projects Using Social Network Analysis." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 6 (2018): 35–46. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.36.3003.

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This paper aims to propose a framework that puts the stakeholders at the forefront of achieving sustainability in the social context. This research, thus, argues that the social sustainability outcomes in construction are best achieved by taking into account the satisfaction of the stakeholders. Based on sustainability and equity theories, a dynamic assessment model has been developed to evaluate the contributions of projects in a social context. Multiple stakeholders and their differing interests associated with the construction projects have been integrated using social network analysis. The mapping of the relationships between the project stakeholders, with respect to their relative stakes and seven social core functions, have been integrated into the assessment model. The findings of this research suggest that the degree of satisfying the needs of diverse stakeholders is highly significant in achieving social sustainability performance of projects. Using a case study from Saudi Arabia, the applicability and significance of the assessment model has been demonstrated. The application of the model provides the opportunity to identify any problems and to enhance the overall performance of projects in the social context. The functionality and efficacy of the model need to be further tested outside the Saudi Arabian region. The research is original in the sense that for the first time, a novel approach has been developed, putting the stakeholders at the forefront of achieving sustainability outcomes in construction projects

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14

Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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15

AYDIN, Nursen. "Social Network Analysis: Literature Review." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 73–80. http://dx.doi.org/10.5824/1309-1581.2018.4.005.x.

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In this article, social network analysis SNA is defined and historical development process is explained. A comprehensive literature search has been conducted for this purpose. SAA is a powerful method that centralizes individuals and their relations, in that the effect of the individual on the social network can be uncovered and the network of individual groups can be evaluated holistically. SNA shows the structural gaps and social capital in institutions, and focuses managers' attention on critical informal networks. Evaluating strategically important networks within an organization, make "invisible" groups visible in the interaction and allows them to work with key groups to facilitate effective collaboration.

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16

S., Geetha. "Big Data Analysis - Cybercrime Detection in Social Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 147–52. http://dx.doi.org/10.5373/jardcs/v12sp4/20201476.

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17

Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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18

Davel, Ronel, Adeline S. A. Du Toit, and Martie M. Mearns. "Understanding Knowledge Networks Through Social Network Analysis." International Journal of Knowledge Management 13, no. 2 (April 2017): 1–17. http://dx.doi.org/10.4018/ijkm.2017040101.

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Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour to illustrate via this question that cultivating synergies between SNA, CoPs and knowledge maps will enable organisations to produce stronger knowledge networks and ultimately increase their social capital. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.

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19

Del Fresno García, Miguel. "Connecting the Disconnected: Social Work and Social Network Analysis. A Methodological Approach to Identifying Network Peer Leaders." Arbor 191, no. 771 (February 28, 2015): a209. http://dx.doi.org/10.3989/arbor.2015.771n1011.

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20

Tekşen, Kerem, and Necati Cemaloğlu. "Mobbing and Social Network Analysis." Technium Social Sciences Journal 39 (January 8, 2023): 184–94. http://dx.doi.org/10.47577/tssj.v39i1.8214.

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The aim of this research is to find out the level of mobbing experience of teachers working in educational institutions and to determine the network characteristics of both the social networks of the organizations where mobbing behavior is common and the participants in these networks. The target population of the research consists of teachers working in a province in Türkiye. The sample of the population was determined by cluster sampling method. In total, 376 teachers in 30 schools were reached, but 11 questionnaires were removed during the pre-analysis data scanning phase, and the remaining 365 questionnaires were analyzed. “Negative Acts Questionnaire” and “Social Network Analysis Questionnaire” were used as data collection tools in the research. SPSS 21.0 and UCINET 6 statistical package programs were used for the analysis of the data obtained in the research, and "frequency", "mean" and "multi-network measurements" were used in data analysis. As a result of the research, it is determined that the average level of mobbing experience of teachers in the organizations participating in the research is low. In addition, three organizations where mobbing is common is determined and the social network structures of these organizations is examined. It is observed that the average degrees and network densities are generally low in these organizations. In addition, these organizations generally show a low level of transitivity. In addition, it is evaluated that some of the participants in the social networks of these organizations may be victims of mobbing, considering that they have a low overall degree. As a support to this finding, it is observed that the participants in question have higher internal and external closeness, low betweenness and low eigenvector values compared to other participants in the organization.

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21

Westaby, James D. "Modeling Massive Social Network Problem Solving via Network Goal Analysis vs. Social Network Analysis." Academy of Management Proceedings 2020, no. 1 (August 2020): 14393. http://dx.doi.org/10.5465/ambpp.2020.14393abstract.

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22

Williams, Trenton A., and Dean A. Shepherd. "Mixed Method Social Network Analysis." Organizational Research Methods 20, no. 2 (July 8, 2016): 268–98. http://dx.doi.org/10.1177/1094428115610807.

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This article outlines a mixed method approach to social network analysis combining techniques of organizational history development, inductive data structuring, and content analysis to offer a novel approach for network data construction and analysis. This approach provides researchers with a number of benefits over traditional sociometric or other interpersonal methodologies including the ability to investigate networks of greater scope, broader access to diverse social actors, reduced informant bias, and increased capability for longitudinal designs. After detailing this approach, we apply the method on a sample of 143 new ventures and suggest opportunities for general application in entrepreneurship, strategic management, and organizational behavior research.

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23

Wetherell, Charles. "Historical Social Network Analysis." International Review of Social History 43, S6 (December 1998): 125–44. http://dx.doi.org/10.1017/s0020859000115123.

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In the past two decades, social network analysis (SNA) has become a major analytical paradigm in sociology and now occupies a strategic place in disciplinary debates on a wide variety of issues. Historians, however, have been slow to adopt the approach for at least three reasons. First, the conceptual orientation of sociologists practicing historical social network analysis (HSNA) remains unfamiliar to the majority of professional historians. Just when SNA was maturing in the late 1980s and 1990s, the interdisciplinary interest in social science theory among historians, so characteristic of the 1970s and early 1980s, began to wane. The subsequent turn toward post modernist thinking in history left the profession increasingly uninformed about both classical and contemporary social theory. Second, those quantitatively-oriented historians who might be predisposed to use SNA's specialized statistical methods constitute less than a quarter of the profession today, thus the risk of SNA finding its way into mainstream historical scholarship is low to start. Third, SNA's data requirements are formidable. SNA demands evidence of social interaction among all members of a social system for a variety of behaviors, and thus necessitates a broad range of high-quality records for the place, time and activities being studied. Because historians are plagued by an incomplete historical record and imperfect understandings of past social relations, HSNA remains an inherently problematic enterprise. Yet despite conceptual, methodological and evidentiary obstacles, SNA possesses real potential for historical analysis.

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24

Herz, Andreas, and Claudia Olivier. "Transnational Social Network Analysis." Transnational Social Review 2, no. 1 (January 2012): 11–29. http://dx.doi.org/10.1080/21931674.2012.10820711.

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25

Georgiou, Ion. "Teaching social network analysis." International Journal of Management Education 21, no. 2 (July 2023): 100816. http://dx.doi.org/10.1016/j.ijme.2023.100816.

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26

KC, Birendra, Duarte B. Morais, M. Nils Peterson, Erin Seekamp, and Jordan W. Smith. "Social network analysis of wildlife tourism microentrepreneurial network." Tourism and Hospitality Research 19, no. 2 (June 30, 2017): 158–69. http://dx.doi.org/10.1177/1467358417715679.

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Social networks are an important element of entrepreneurship. Entrepreneurs rely on social networks to access ideas, information, and resources to facilitate their entrepreneurial process. Strong and weak ties influence the entrepreneurial process in unique ways. This study utilized social network analysis approach to examine wildlife tourism microentrepreneurship through in-person structured interviews with 37 microentrepreneurs from North Carolina’s Pamlico Sound Region. Specifically, this study examined the extent of network ties, the type of support received from those network ties, and the process of creating and maintaining the business network ties. Weak ties were more prevalent than strong ties. Support was received in terms of marketing and advertising, information sharing, and product sponsorship. Weak ties were established through professional workshops and seminars or while working in the same territory, whereas reciprocity, togetherness, communication, and trust were identified as major factors to maintain weak ties. This study suggests that cognitive social capital factors (e.g. reciprocity, togetherness, and trust) can be highly important toward effective use of social networks, as well as to ensure entrepreneurial success.

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27

Brass, Daniel J. "New Developments in Social Network Analysis." Annual Review of Organizational Psychology and Organizational Behavior 9, no. 1 (January 21, 2022): 225–46. http://dx.doi.org/10.1146/annurev-orgpsych-012420-090628.

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This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?”

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28

Skvoretz, John. "Pas de Deux: Social Networks and Network Analysis." Contemporary Sociology: A Journal of Reviews 37, no. 5 (September 2008): 423–26. http://dx.doi.org/10.1177/009430610803700511.

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29

Himelboim, Itai, Marc A. Smith, Lee Rainie, Ben Shneiderman, and Camila Espina. "Classifying Twitter Topic-Networks Using Social Network Analysis." Social Media + Society 3, no. 1 (January 2017): 205630511769154. http://dx.doi.org/10.1177/2056305117691545.

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As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.

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Schwartz, Daniel M., and Tony (D.A.) Rouselle. "Using social network analysis to target criminal networks." Trends in Organized Crime 12, no. 2 (October 24, 2008): 188–207. http://dx.doi.org/10.1007/s12117-008-9046-9.

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31

Grunspan, Daniel Z., Benjamin L. Wiggins, and Steven M. Goodreau. "Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research." CBE—Life Sciences Education 13, no. 2 (June 2014): 167–78. http://dx.doi.org/10.1187/cbe.13-08-0162.

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Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.

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Hong-lin, Xu, Yan Han-bing, Gao Cui-fang, and Zhu Ping. "Social Network Analysis Based on Network Motifs." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/874708.

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Based on the community structure characteristics, theory, and methods of frequent subgraph mining, network motifs findings are firstly introduced into social network analysis; the tendentiousness evaluation function and the importance evaluation function are proposed for effectiveness assessment. Compared with the traditional way based on nodes centrality degree, the new approach can be used to analyze the properties of social network more fully and judge the roles of the nodes effectively. In application analysis, our approach is shown to be effective.

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33

Crossley, Nick. "Exploratory Social Network Analysis with Pajek, Models and Methods in Social Network Analysis." Sociology 40, no. 5 (October 2006): 965–68. http://dx.doi.org/10.1177/0038038506067527.

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34

Utami, Sabrina Rahma, Rika Nurismah Safitri, and Yohanes Ari Kuncoroyakti. "Network Analysis and Actors #CancelOmnibusLaw on Twitter Social Media Using Social Network Analysis (SNA)." JCommsci - Journal Of Media and Communication Science 4, no. 3 (December 29, 2021): 135–48. http://dx.doi.org/10.29303/jcommsci.v4i3.111.

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Omnibus Law is the merging of several different rules into one law. RUU Cipta Kerja is one part of the Omnibus Law that attracts attention because it is considered detrimental to society. This caused a lot of rejection and protests from the society. The protest was held directly in the form of demonstrations in various regions of Indonesia and also in Twitter through #BatalkanOmnibusLaw. The purpose of this research is to find out the analysis of communication networks and identify influential actors in #BatalkanOmnibusLaw on Twitter. This research uses Social Network Analysis (SNA) methods and Computer-mediated Communication theory. Data is collected through Twitter from August 1-October 31, 2020. The process of analyzing and retrieving data is using Netlytic.org and Gephi software. The results showed that there were 62 actors with 153 interactions. Proximity between actors is worth 3, meaning close proximity and easy interaction between actors. The interactions created between actors are very few, uneven ,and the interactions that occur only one way. The #BatalkanOmnibusLaw is centered on ten actors, the most dominant account is @fraksirakyatid. Based on degree centrality analysis, closeness centrality, betweenness centrality, and eigenvector centrality the most influential actors in #BatalkanOmnibusLaw network are @fraksirakyatid and @walhinasional. Keywords: #BatalkanOmnibusLaw, Twitter, Actor, Communication Network

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35

Andryani, Ria, Edi Surya Negara, Rezki Syaputra, and Deni Erlansyah. "Analysis of Academic Social Networks in Indonesia." Qubahan Academic Journal 3, no. 4 (December 9, 2023): 409–21. http://dx.doi.org/10.58429/qaj.v3n4a289.

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Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.

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S, Santhosh Kumar, Vishnu Vardhan S, Wasim Jaffar M, Sultan Saleem A, and Sharmasth Vali Y. "Social Communicative Extraction Analysis." International Research Journal of Multidisciplinary Technovation 2, no. 4 (September 26, 2020): 4–10. http://dx.doi.org/10.34256/irjmt2042.

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The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.

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37

Arif, Tasleem. "The Mathematics of Social Network Analysis: Metrics for Academic Social Networks." International Journal of Computer Applications Technology and Research 4, no. 12 (November 26, 2015): 889–93. http://dx.doi.org/10.7753/ijcatr0412.1003.

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38

Kim, Jooho, and Makarand Hastak. "Social network analysis: Characteristics of online social networks after a disaster." International Journal of Information Management 38, no. 1 (February 2018): 86–96. http://dx.doi.org/10.1016/j.ijinfomgt.2017.08.003.

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39

Gadiparthi, Manjunath, and E. Srinivasa Reddy. "Impact of Individuals’ Engagement in Social Network-An Extensive Analysis." Webology 19, no. 1 (January 20, 2022): 2782–96. http://dx.doi.org/10.14704/web/v19i1/web19185.

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Social Network (SN) is of avail for sharing information among individuals and communities for different purposes like sharing opinions, feelings, photos, videos and many others. Since the start of the COVID-19 epidemic and the ensuing limitations, the use of Apps on smart devices has exploded. In-line with how much time is spent on SN by a person, the manifestation of physical and mental problems are found in diverse patterns. In this review a comparative account is presented linking the time spent by individuals on social network and the patterns of the resultant health problems in course of time. Most of the earlier studies categorize the users in to various groups based on the time spent on social network. Then they describe the apparent problems that are faced, under two categories, due to the extensive time spent by the users on various social network applications. Finally, the review presents a comprehensive idea of the different analytical techniques used for finding problems faced with respect to the time spent and frequency of social network use. The results on the whole present a variegated picture as regards existence of correlation between intensity of usage and incidence of health problems.

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40

UGURLU, Zeynep. "Social Network Analysis of the Farabi Exchange Program: Student Mobility." Eurasian Journal of Educational Research 16, no. 65 (October 17, 2016): 1–35. http://dx.doi.org/10.14689/ejer.2016.65.18.

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41

Gillieatt, Sue, Christina Fernandes, Angela Fielding, Antonia Hendrick, Robyn Martin, and Susi Matthews. "Social Network Analysis and Social Work Inquiry." Australian Social Work 68, no. 3 (June 11, 2015): 338–51. http://dx.doi.org/10.1080/0312407x.2015.1035660.

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42

Rowley, Timothy J. "Social Network Analysis in Action." Proceedings of the International Association for Business and Society 9 (1998): 671–81. http://dx.doi.org/10.5840/iabsproc1998963.

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43

Maltseva, Daria, and Vladimir Batagelj. "Journals publishing social network analysis." Scientometrics 126, no. 4 (February 25, 2021): 3593–620. http://dx.doi.org/10.1007/s11192-021-03889-z.

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44

Boorman, Scott A., Paul R. Levitt, and Ronald S. Burt. "Pitfalls in Social Network Analysis." Contemporary Sociology 14, no. 4 (July 1985): 419. http://dx.doi.org/10.2307/2069152.

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45

Pappi, Franz Urban, and John Scott. "Social Network Analysis: A Handbook." Contemporary Sociology 22, no. 1 (January 1993): 128. http://dx.doi.org/10.2307/2075047.

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Youssef, Bassant E. "Online Social Network Internetworking Analysis." International Journal of Next-Generation Networks 6, no. 2 (June 30, 2014): 1–15. http://dx.doi.org/10.5121/ijngn.2014.6201.

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Berkowitz, S. D., Peter V. Marsden, and Nan Lin. "Social Structure and Network Analysis." Social Forces 63, no. 3 (March 1985): 854. http://dx.doi.org/10.2307/2578496.

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Musial, Katarzyna, Piotr Brodka, and Matteo Magnani. "Social Network Analysis in Applications." AI Communications 29, no. 1 (October 6, 2015): 55–56. http://dx.doi.org/10.3233/aic-150688.

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Vermunt, Jeroen K., and Marijtje A. J. van Duijn. "Special Issue: “Social Network Analysis”." Methodology 2, no. 1 (January 2006): 1. http://dx.doi.org/10.1027/1614-2241.2.1.1.

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Mills, Barbara J. "Social Network Analysis in Archaeology." Annual Review of Anthropology 46, no. 1 (October 23, 2017): 379–97. http://dx.doi.org/10.1146/annurev-anthro-102116-041423.

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