Meta-analysis on school level variables in a co-evolving network analyses of adolescent friendship and smoking behaviour McAneney, H., Cochrane, G., Higgins, K., McCann, J., & Kee, F. (2014). Meta-analysis on school level variables in a co-evolving network analyses of adolescent friendship and smoking behaviour. 354. Poster session presented at XXXIV Sunbelt Social Networks Conference of the International Network for Social Network Analysis (INSNA), St Pete Beach, United States. Document Version: Publisher's PDF, also known as Version of record Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights Copyright the authors 2014. 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Oct. 2021 Abstracts for Sunbelt XXXIV International Network for Social Network Analysis TradeWinds Island Resorts, St. Pete Beach, FL February 18 - 23, 2014 Abstracts for Sunbelt XXXIV 1 2-Mode Networks WED.PM1 2-Mode Networks WED.PM1 Communities in Multi-Mode and Multi-Level Networks: Some Insights from Synthetic Data David Melamed, University of South Carolina, Department of Sociology Contact: [email protected] The literature on discovering communities in networks, or densely connected subsets of vertices within the network, has exploded in the past decade (Fortunato, 2010). Almost all of this research, however, identifies communities in one-mode networks (e.g., person-to-person networks). Very little research has examined the identification of community structures in multi-mode or multi-level networks (c.f., Barber, 2007; Mucha et al., 2010; Melamed et al. 2013). In an effort to address this shortcoming in the literature, we describe how to generalize three commonly used algorithms for the identification of communities in one-mode networks to multi-mode and multi-level networks: Girvan and Newman’s (2002) betweenness-based algorithm, Newman’s (2006) spectral partitioning, and simulated annealing (e.g., Kirkpatrick 1984). To evaluate the algorithms, we rely on extensive network simulations. Results of the simulations reveal that spectral partitioning does not perform as well as the other algorithms. Results also indicate that the algorithms are more effective on denser networks – having more information to exploit aids the identification of the underlying community structure. We conclude with a discussion of directions for further research, including other community detection algorithms that would benefit from generalization to multi-mode and multi-level networks. 2-Mode Networks WED.PM1 Professions as skill structures: Institutionalizing knowledge of data science Philipp Brandt, Columbia University Contact: [email protected] This study explores the relationship between skill densities and the emergence of professions. A method is developed using skills mentioned in job descriptions that are representative of a certain profession. These skills serve as features in a logistic classifier that is trained to identify job descriptions mentioning skills of a certain profession regardless of the specific title. This method is applied to a series of cases, including the canonical cases of legal and social work, Abstracts for Sunbelt XXXIV 2 medicine, and the pseudo--profession of project management. The method achieves a precision of around 90 to 95 percent for proper professions, but much lower scores for pseudo- professions. These results confirm theories differentiating expert work from other types by having a formalized knowledge base. The proper professions are then transformed into a position--skills co--occurrence network to analyze the meso--structure of expert knowledge. "Data science" is studied as the focal case of successful professionalization. It is compared to legal work as the baseline and risk analysts as counterfactual case. Community detection analyses indicate similar modularity scores for all cases, but accounting for densities within and between concrete clusters reveals higher centralization of failing professions and closure in the case of successfully institutionalized and institutionalizing professions. This study contributes a new methodological strategy that overcomes existing limitations to quantitatively study professional labor with traditional data by proposing a classifier for unstructured data. Substantively, block models show that the informal knowledge structure of a profession varies independent of it's degree of formal organization. Abstracts for Sunbelt XXXIV 3 Centrality in Social Networks WED.PM1 Centrality in Social Networks WED.PM1 Defining Network Positions Ulrik Brandes, Unversity of Konstanz Contact: [email protected] As part of a grander scheme to advance the methodology of network analysis, a precise notion of position is proposed. The definition is sufficiently general to integrate a range of network- analytic concepts (e.g., social capital, centrality, role, or brokerage) into a framework of positional comparison. Since they are retained as special cases, previous methods are not replaced but related in novel ways, with certain gaps filled in. By explicating otherwise hidden assumptions and unwitting decisions, the framework also points to the loci for substantive theory and interpretation fallacies. Examples demonstrate that, despite its abstract look and feel, the approach is rather practical. Centrality in Social Networks WED.PM1 Growth, Stability, Change and Inequality in Dynamic Ego Networks David Hachen, University of Notre Dame, Omar Lizardo Contact: [email protected] It is well-known that in social networks there is inequality in vertex degree with some people having much larger ego networks than others. However there is debate about how such inequalities emerge and why some people have more social ties than others. With fine-grained temporal data on the formation and decay of social ties, we distinguish between four ego network traits related to degree: cumulative degree, effective degree, active degree, and churn. After conceptually and operationally distinguishing between these four traits, we focus on effective degree, the number of people a person could interact with at a given point in time. Using behavioral data on social ties gathered from analyses of communication events (texts and voice calls) of 161 students during their first academic year at the University of Notre Dame, we map out how student’s ego networks involving other ND students grow, stabilize and change over 39 weeks. Analyses reveal that (1) after an initial growth period, effective degree stabilizes, (2) when it stabilizes, effective degree evidences inequality, and (3) the most important predictor of this inequality is how outgoing a student is, measured by an extroversion scale administered prior to a student’s arrival at ND. These findings call into Abstracts for Sunbelt XXXIV 4 question preferential attachment models of growth in vertex degree that stipulate that inequality in degree occurs through an endogenous popularity tournament process. Instead an exogenous factor, a person’s capacity to be sociable, predicts initial effective degree, and because effective degree is fairly stable, a student’s effective degree at the end of their first academic year. Centrality in Social Networks WED.PM1 Some properties and extensions of PN centrality Martin Everett, University of Manchester, Stephen Borgatti Contact: [email protected] Recently Everett and Borgatti have proposed a new centrality measure, PN centrality for networks that contain positive and negative ties. We examine the performance of PN centrality and give extreme examples which highlight its potential. In addition we look at some of the parameters that are fixed to normalize the measure and suggest alternatives which may prove useful for different types of data. Finally we suggest some related alternatives which are part of the same family but use different underlying assumptions. Centrality in Social Networks WED.PM1 Subgroup Centrality: Methods and Examples Jocelyn Bell, United States Military Academy Contact: [email protected] In a social network consisting of nodes with different attributes or community groupings, identifiable subgroups emerge that intra- and inter-relate. We present a method for calculating centrality measures with respect to subgroups defined by these nodal attributes. For example, using the attribute of sex, we can ask questions such as “which women are most influential over the men?” or “which men help sustain the communication of the group of women?” Often, traditional centrality measures fall short of answering such questions. Our subgroup centrality measuring method uncovers previously hidden network features, as evidenced by our discussion and analysis of a classic social network. Abstracts for Sunbelt XXXIV 5 Collective Actions and Social Movements WED.PM1