Communications of the ACM
Researchers Provide Insight Into the Future of How We Understand Social Networking
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University of Minnesota researchers have found that analyzing social networks can lead to breakthroughs in different aspects of social interactions, including the emergence or decline of leadership, changes in trust over time, and mobility within certain online communities. It might be easier to understand why, when, and how users are friends with each other, if new factors such as changes across time and space can be considered, say Minnesota professor Shashi Shekhar and research assistant Dev Oliver in their paper, "Computational Modeling of Spatio-temporal Social Networks: A Time-Aggregated Graph Approach." They say their research could be useful to business and software developers using career networking sites such as LinkedIn.
Human resource professionals could use the data to cross-reference an individual's contacts to determine if a certain contact was established during a specific time frame, the researchers say.
These new developments highlight the need for "a central role for computation and computational models, not only to scale up to the large and growing data volumes, but also to address new spatio-temporal social questions related to change, trends, duration, mobility, and travel," according to Shekhar and Oliver.
From University of Minnesota News
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