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This paper provides details about a historical dataset of Canadian corporations and business elites who served on corporate boards circa 1912. The source of this corporate interlock data is the <italic>Directory of Directors in Canada, 1912</italic>, a public domain volume listing Canadian public companies in Canada. Because these data are thought to be of interest not only to network researchers, but also to business historians and management scholars, an attempt has been made to make the data as easy to use as possible. Supplementary information has also been added to the network files provided. All of the individuals and companies in the dataset have been geolocated. The proper 1911 Census Division a company was located in has also been added so that the networks can be combined with other publicly available data from the period. Two sets of graph files are provided in CSV format with other formats provided on the author’s website. The first file contains corporations as the nodes with directors as edges. The second file has the individual directors as nodes and edges connecting them are corporate boards individuals both sat on.
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Networks of Canadian Business Elites: Historical Corporate Interlock Networks circa 1912

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This paper introduces the idea of studying the decision citation network of the US Supreme Court in a new fashion by focusing on this Court’s overturning of some of its prior decisions. Two departures from current practices were developed. One was to consider the phenomenon of overturning in a broader network context. The second was to treat the citations between overturning decisions and the overturned decisions as negative ties. This led to the creation of multiple signed citation networks. These networks were studied to get a better understanding of the operation of this Court. The results show that, frequently, when decisions are overturned, this is not done in a logically consistent fashion. A research agenda is proposed regarding a reexamination of <italic>stare decesis</italic>, thought to be a bedrock of the US legal system, and calling it into question as a genuine operating legal principle.
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Signed Networks for the US Supreme Court Overturning its Prior Decisions

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The graphic representation of relational data is one of the central elements of social network analysis. In this paper, the author describe the use of visualization in interview-based data collection procedures designed to obtain personal networks information, exploring four main contributions. First, the author shows a procedure by which the visualization is integrated with traditional name generators to facilitate obtaining information and reducing the burden of the interview process. Second, the author describes the reactions and qualitative interpretation of the interviewees when they are presented with an analytical visualization of their personal network. The most frequent strategies consist in identifying the key individuals, dividing the personal network in groups and classifying alters in concentric circles of relative importance. Next, the author explores how the visualization of groups in personal networks facilitates the enumeration of the communities in which individuals participate. This allows the author to reflect on the role of social circles in determining the structure of personal networks. Finally, the author compares the graphic representation obtained through spontaneous, hand-drawn sociograms with the analytical visualizations elicited through software tools. This allows the author to demonstrate that analytical procedures reveal aspects of the structure of personal networks that respondents are not aware of, as well as the advantages and disadvantages of using both modes of data collection. For this, the author presents findings from a study of highly skilled migrants living in Spain (<italic>n</italic> = 95) through which the author illustrates the challenges, in terms of data reliability, validity and burden on both the researcher and the participants.
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What the eye does not see: visualizations strategies for the data collection of personal networks

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At the business meeting of the International Network for Social Network Analysis (INSNA) in June 2018, there was great enthusiasm for conducting and archiving interviews with luminaries in the field. Lin Freeman’s name was mentioned, and I was drafted to conduct the interview. This is an edited transcript of the interview that I conducted with Lin on July 27, 2018. Lin passed away three weeks later, on August 17, 2018. He was 91 years old.
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Linton C. Freeman Interview by Katherine Faust, July 27, 2018

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The activity facilitates students’ understanding of network measures, including different types of node centrality, shortest paths, cliques, and communities, and their implications for information flow in groups or organizations. The goal of the game is for students (a minimum of 10 and maximum of 28 participants in a network; a larger class can be divided into two or more networks) to solve a company mystery by exchanging information clues with other students based on an imposed communication network configuration. The activity can be debriefed by discussing the game outcomes, analyzing the network structure (using a software to input data and calculate key network measures), and evaluating the practicality of the game. Examples of network configuration, data sets, and a script which uses the igraph package in R are included.
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Who Dunnit: The Party Mystery Game for Analyzing Network Structure and Information Flow

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This research focused on the identification and tracking of subgroups of vessels of interest, owners, operators, ports, cargoes, and specific activities associated with artificial reef enhancement and construction in the South China Sea. Historical automated information system (AIS) tracks and current maritime databases were used to develop sociogram depictions of the gray (licit but only partially transparent) maritime network that connects these nodes (ships, events, organizations, ports, activities). Social network matrices were dynamically updated by open source databases to provide insights into real-time awareness and tracking for operational purposes.
The maritime network data set was populated by, and dynamically updated through, the integration of unclassified data using algorithms developed as part of the research. Longitudinal topographic metrics  –  average degree, average clustering coefficient, and centralization  –  were used to analyze the multi-mode (e.g., ship to ship, ship to owners/operators, owner/operators to owner/operators, ships to locations) relationships within the gray maritime network. Additionally, the network of ports and reefs in the area of operations was mapped and insights were gained by leveraging directed centrality measures  –  hubs and authorities  –  connecting them.
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Mapping Gray Maritime Networks

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Relational geography asserts that social networks provide geographic benefits, and geographies are transmitted through the sharing of local knowledge and experience. To articulate the spatial expanse and geographic benefits of an individual’s social network, researchers require better social-spatial geographic information system models illustrating how contacts are dispersed, and how many distinct places they inhabit.
In this work, the authors conduct a case study to map social network ties in geographic space. The authors retrieve social network matrices for 20 volunteers (egos) via Facebook.com, amounting to over 8,500 friends (alters). Each ego listed the alter’s hometown city at two time periods: at relationship inception and at the time of the study. The authors measure specific tie locations, tie expanse, deviation from a gravity model prediction, and expansion of alter groups (family, clubs, neighbors, etc.) over time.
The authors find that social networks geographically spread over time, on average, from 2,679 km (standard distance) to 3,258 km (standard distance), and that the average ego had alters in 21 unique locations when they met, and 38 locations at the time of the study. Regarding friend groups, the authors discover that high school friends and friends from non-residential gatherings (ex. conferences) dispersed the most (over 1,900 km), and cultural groups (churches, sports teams) and family dispersed the least (less than 800 km) over time. Our results lead to a discussion of how mapping and measuring the distribution of social connections can uncover changing dynamics of social interaction, and one’s ability to access and engage with places through social ties.
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Mapping the Distribution and Spread of Social Ties Over Time: A Case Study Using Facebook Friends

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This paper investigates whether the Maidan Revolution in Kyiv (late 2013–early 2014) and the ongoing armed conflict in Eastern Ukraine (early 2014) have been reflected in the collaboration networks of Ukrainian and Russian organizations in Sweden between 2013 and 2016.
I use ERG models to account for the probabilities of ties between the organizations, depending on the network structure and individual attributes such as ethnic identification and the choice of a side to support in the conflict.
Results suggest that it is support for a certain side in the conflict, and not ethnic self-identification, which drives the clustering of the networks during the most violent period.
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Ukrainian and Russian organizations in Sweden and the conflict “back home”

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Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order <italic>M</italic> plus <italic>N</italic> log <italic>N</italic>, where <italic>N</italic> is the number of nodes and <italic>M</italic> is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.
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Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs

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In order to understand scientists’ incentives to form collaborative relations, we have conducted a study looking into academically relevant resources, which scientists contribute into collaborations with others. The data we describe in this paper are an egocentric dataset assembled by coding originally qualitative material. It is 40 multiplex ego networks containing data on individual attributes (such as gender, scientific degree), collaboration ties (including alter–alter ties), and resource flows. Resources are coded using a developed inventory of 25 types of academically relevant resources egos and alters contribute into their collaborations. We share the data with the research community with the hopes of enriching knowledge and tools for studying sociological and behavioral aspects of science as a social process.
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Academic Collaboration via Resource Contributions: An Egocentric Dataset

AHEAD OF PRINT

Volume 44 (2024): Issue 1 (January 2024)

Volume 43 (2023): Issue 1 (January 2023)

Volume 42 (2022): Issue 1 (January 2022)

Volume 41 (2021): Issue 1 (January 2021)

Volume 40 (2020): Issue 1 (January 2020)

Volume 39 (2019): Issue 1 (January 2019)

Volume 38 (2018): Issue 1 (January 2018)

Volume 37 (2017): Issue 1-2 (January 2017)

Volume 36 (2016): Issue 1 (January 2016)

Connections

Connections publishes original empirical, theoretical, and methodological articles, as well as critical reviews dealing with applications of social network analysis. From volume 2018 the journal is published in a continous format.  The research spans many disciplines and domains including: Anthropology, Sociology, Psychology, Communication, Economics, Organizational Behavior, Knowledge Management, Marketing, Social Psychology, Mathematics, Public Health, Medicine, Computer Science, and Policy.  As an official journal of the International Network for Social Network Analysis, the emphasis of the publication is to reflect the ever-growing and continually expanding community of scholars using network analytic techniques. Connections also provides an outlet for sharing news about social network concepts and techniques and new tools for research.  Rejection rate: 60%  Open Access Policy  This journal provides immediate open access to its content under the Creative Commons CC BY 4.0 license on the principle that making research freely available to the public supports a greater global exchange of knowledge. Under the Creative Commons CC BY 4.0 license users are free to share (copy and redistribute the material in any medium or format) and adapt the work (remix, transform, and build upon the material for any purpose, even commercially) if the contribution was properly attributed and all of the changes indicated.  ABOUT SOCIETY  International Network for Social Network Analysis is the professional association for researchers interested in social network analysis. The association is a non-profit organization incorporated in the state of Delaware and founded by Barry Wellman in 1977.  INSNA was founded on the premise that the behavior and lives of social entities are affected by their position in the overall social structure. By examining the etiology and consequences of structural forms overall, of the location of entities within these structures, and of the formation and dynamics of ties that make up these structures, INSNA hopes to learn about the parts of behavior that are uniquely social.  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process.