Role of Temporal Diversity in Inferring Social Ties Based on Spatio-Temporal Data
Deshana Desai, Harsh Nisar, Rishab Bhardawaj

TL;DR
This paper introduces a new spatio-temporal dataset and demonstrates that Temporal Diversity significantly improves the inference of social tie strength from location data, revealing insights into student interactions within a university campus.
Contribution
The study presents DSSN, a detailed location dataset, and introduces Temporal Diversity as a novel metric that enhances social relationship inference from spatiotemporal data.
Findings
Temporal Diversity outperforms other metrics in predicting relationship strength.
Bounded geographical areas like university campuses provide valuable microcosms for social interaction studies.
Evolving Temporal Diversity offers insights into dynamic social relationships over time.
Abstract
The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal dataset of 0.7 million data points of continuous location data logged at an interval of every 2 minutes by mobile phones of 46 subjects. Our research is focused at inferring relationship strength between students based on the spatiotemporal data and comparing the results with the self-reported data. In that pursuit we introduce Temporal Diversity, which we show to be superior in its contribution to…
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Taxonomy
TopicsComplex Network Analysis Techniques · Human Mobility and Location-Based Analysis · Opinion Dynamics and Social Influence
