Answer The Following Three Questions In One To Two Paragraph ✓ Solved

Answer the following three questions in one to two paragraph

Answer the following three questions in one to two paragraphs each, focusing on network theory concepts and implications:

1) What is centrality in a network context? What is the difference between degree centrality and closeness centrality? When might we want to use a measure like closeness centrality to discuss someone’s position in a social network instead of degree centrality? What about transitivity? When might we care more about transitivity than we do about centrality as researchers?

2) What is contagion in a network context? Describe one example of network contagion in your own life. In your example, identify the social network (a network of classmates? a family? a workplace?), the nature of the connection between individuals (do you exchange information? affection? money-labor?), the directionality, and the thing spreading through the network.

3) Much of the research with which we have engaged so far is now over a decade old (if not older). What more recent changes—social changes, technological innovations, political phenomena, etc.—may have changed some of the conventional wisdom, findings, or scientific understandings of social networks since this research was carried out? Please describe at least one change and walk us through what the consequences of that change might be on current and future social network scholarship.

Paper For Above Instructions

Centrality in social networks is a family of measures that assesses how important or influential a node is based on its position within the network structure. The classic distinction begins with degree centrality, which simply counts the number of direct ties a node has, providing a local sense of prominence that is easy to interpret but can be misleading in networks where peripheral but well-connected clusters exist. In contrast, closeness centrality uses the lengths of the shortest paths from a node to all others (often taking the reciprocal of the sum of these distances) to capture how quickly a node can reach or influence the rest of the network; this is a global measure and can identify nodes that act as efficient conduits for information flow even if their immediate degree is modest. However, closeness can be unstable in sparse or disconnected networks, where some distances are infinite or ill-defined, complicating comparisons across individuals or groups (Freeman, 1979; Newman, 2010). In practice, researchers also leverage other centrality notions—such as betweenness and eigenvector centrality—to capture whether a node sits on many shortest paths or connects to other highly connected nodes, respectively, each emphasizing different notions of influence and reach (Freeman, 1979; Newman, 2010).

Transitivity, or clustering, concerns the tendency of a node’s neighbors to connect with one another, forming triangles and cohesive subgroups. This triadic closure shapes how information, norms, and behaviors circulate locally and can create stable micro-communities within larger networks. From a research perspective, transitivity matters when the goal is to understand local cohesion, trust, or redundancy of connections. A network with high transitivity can support robust diffusion within clusters even if a globally central node is absent; conversely, low transitivity can indicate looser ties and greater exposure to diverse but less interconnected groups. Thus, depending on the research question—global reach versus local cohesion—transitivity can be as important as, or more important than, centrality in describing social structure (Granovetter, 1973; Wasserman & Faust, 1994).

Contagion in a network context refers to how ideas, behaviors, or states propagate across social ties, shaped by social influence, imitation, information exchange, or other mechanisms of transmission. The study of contagion emphasizes how network structure—who is connected to whom, and how densely those connections knit together—can accelerate or impede diffusion. Classic work shows that not only the presence of ties but their arrangement matters; highly connected hubs can speed diffusion, while dense clusters can foster rapid adoption within communities, potentially creating patchy but intense waves of contagion (Centola, 2010; Christakis & Fowler, 2007; Keeling & Eames, 2005).

In my life, a concrete example of network contagion occurred when I observed the adoption of a daily walking habit among coworkers in my workplace. The social network consisted of colleagues who interacted informally during breaks and on projects, with relationships primarily characterized by information exchange and social influence (bidirectional in most cases). The thing spreading was the habit of taking a short walking break during lunch. The diffusion began with a few early adopters and then propagated to adjacent teams through ongoing interactions, illustrating how behavior can diffuse through a real-world workplace network and how local clusters can reinforce adoption through repeated contact and social encouragement (Centola, 2010; Christakis & Fowler, 2007).

Since foundational network research was conducted, several shifts have influenced how scholars study networks. The rise of online platforms and algorithmic mediation reshapes who is connected to whom and how information travels, compelling researchers to account for platform effects, filtering, ranking, and recommendations when interpreting centrality and diffusion patterns (Barabási, 2016; Easley & Kleinberg, 2010). These online traces enable large-scale analyses of diffusion, timing, and network topology that were not feasible with traditional social network data, encouraging a move toward dynamic and, in some cases, multiplex perspectives on networks (Newman, 2010; Keeling & Eames, 2005).

In addition, the COVID-19 era highlighted the practical relevance of network ideas for understanding contact patterns and disease spread, prompting broader adoption of temporal and multilayer approaches in network science. As a result, contemporary scholarship increasingly emphasizes time-varying ties and multiple types of relationships (e.g., information, affection, collaboration) rather than static, single-layer graphs. These developments have substantial consequences for measuring centrality and transitivity, as well as for interpreting contagion, because time and tie diversity can fundamentally alter diffusion pathways and the identification of influential actors (Newman, 2010; Barabási, 2016).

Overall, these changes imply that centrality measures may lose some interpretive power if applied naively to static, single-layer networks. Researchers now routinely consider temporal dynamics, multiplexity, and context-specific tie meanings, and they increasingly rely on methods that can disentangle social influence from homophily and other confounds. The literature suggests a shift toward more nuanced models of influence and diffusion, using dynamic networks, richer tie-typing, and causal inference tools to better apprehend contemporary social processes (Freeman, 1979; Wasserman & Faust, 1994; Easley & Kleinberg, 2010).

References

1. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarifications. Social Networks, 1(3), 215-239.
2. Granovetter, M. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380.
3. Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
4. Newman, M. E. J. (2010). Networks: An Introduction. Oxford: Oxford University Press.
5. Jackson, M. O. (2008). Social and Economic Networks. Princeton University Press.
6. Barabási, A.-L. (2016). Network Science. Cambridge: Cambridge University Press.
7. Centola, D. (2010). The Spread of Behavior in an Online Social Network. Science, 329(5996), 1194-1197.
8. Christakis, N. A., & Fowler, J. H. (2007). The Spread of Obesity in a Large Social Network. New England Journal of Medicine, 357(4), 370-379.
9. Keeling, M. J., & Eames, K. T. (2005). Networks and Epidemic Models. Journal of the Royal Society Interface, 2(4), 295-307.
10. Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge: Cambridge University Press.