Comparing Community Structure to Characteristics in Online Collegiate Social Networks

By Amanda L. Traud, Eric D. Kelsic, Peter J. Mucha, M. A. Porter. SIAM Review, 2008

474 citations recorded by the source corpus.

We study the structure of social networks of students by examining the graphs of Facebook “friendships” at five U.S. universities at a single point in time. We investigate the community structure of each single-institution network and employ visual and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the employed pair-counting indices and recall, in simplified notation, a useful formula for the $z$-score of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form “communities,” and leads to comparative observations about online social structures, which reflect offline social structures. We calculate the relative contributions of different characteristics to the community structure of individual universities and compare these relative contributions at different universities. For example, we examine the importance of common high school affiliation at large state universities and the varying degrees of influence that common major can have on the social structure at different universities. The heterogeneity of the communities that we observe indicates that university networks typically have multiple organizing factors rather than a single dominant one.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3476aeb6116e98eed3135c273e4489e5265434e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Core-Periphery Structure in Networks (2012, background): “Investigations of community structure have led to insights in applications such as committee [50] and voting [40] networks in political science, friendship networks at universities [61] and other schools [29], protein-protein interaction networks [38], and mobile telephone networks [45].”
Graph Clustering with Graph Neural Networks (2020, background): “nomena in the underlying graph, for example to education [56] or employment [40] in social graphs.”
Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, unknown): “These well-learned sequences are known to be generated by ‘‘core’’ areas [8,22–24].”
Community detection in networks: A user guide (2016, background): “We stress, however, that the measure may not be suitable when the partitions to be compared are very dissimilar from each other [70] and that it shows unintuitive behaviour in particular instances [88].”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “For each resolution parameter, we quantified the similarity of all pairs of community assignments as the z-score of the Rand index, and selected the parameter (γ = 1.8) with the greatest average similarity (Traud et al. 2011).”
Multi-scale brain networks (2016, unknown): “, 2009), variation of information (Meilă, 2003), or the z-score of the Rand coefficient (Traud et al., 2011).”
Social Structure of Facebook Networks (2011, unknown): “[29], in which we introduced the community comparison methods that we employ below.”
Modular Brain Networks (2015, background): “However, because the precise values of these similarity measures are often difficult to interpret, it is good practice to use the z-scores, rather than the raw scores, of these measures (Traud et al. 2011).”
Intrinsic and task-evoked network architectures of the human brain. (2014, unknown): “To examine the relationship between these community partitions and a previously defined resting-state FC community partition (Power et al., 2011) (Figure 2B), we calculated the partition similarity using the Z score of the Rand coefficient (Traud et al., 2011).”
Mapping the human brain’s cortical-subcortical functional network organization (2018, unknown): “Stability estimates were computed as the z-rand partition similarity (Traud et al., 2011) averaged across all iterations for a given gamma value.”
Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015, methodology): “To quantify these results, we compared the consensus partitions for both conditions and each subject separately for frontal and nonfrontal regions using a z score of the Rand coefficient (25, 26), which quantifies the similarity between two partitions.”
Digital Footprints: Opportunities and Challenges for Online Social Research (2014, background): “For example, Golder et al. (2007) showed that private messaging by nonfriends took place primarily at late-night hours, Traud et al. (2010) compared the network structures of multiple universities, and Mayer & Puller (2008) modeled tie formation within one university.”