# Visual Analytic Method for Students' Association via Modularity Optimization
## 2 Related Work
### Constructing Social Networks via Spatiotemporal Data  
Much current research infers social ties between people from spatiotemporal behavior data, based on the assumption that two people should know each other if they have been in the same location at the same time on multiple occasions. However, is this assumption true? To answer this question, [PERSON] et al. [10] proposed a probabilistic model to quantify the correlation between the number of co-occurrences and the strength of social ties, and their experimental results proved that the number of co-occurrences has significant implications in forming inferences about social ties, which lays a foundation for related works.  
To address the shortcomings of straightforward methods (richness and frequency), [PERSON] et al. [11] proposed an entropy-based model that infers the social connections and quantifies their strength by analyzing people's spatiotemporal co-occurrences. Their method uses Renyi entropy to measure the diversity of co-occurrences. A parameter controls how much coincidences contribute to diversity, and a weighted frequency can increase the impact of co-occurrences in uncrowded locations on the strength of social connections. It utilizes location semantics to improve the model. The experimental results show that it outperforms its counterparts and can be applied in networks, such as computing social strength in the social internet of things [12] and recommending friends in location-based social networks [13].  
Based on the co-occurrences of behavior, [PERSON] et al. [14] proposed that the impact of a given location on social strength should vary by time period. They exploited four spatiotemporal features, including fine-grained temporal features, weekday and weekend features, fine-grained location weight, and co-occurrence features, to infer friendship. [PERSON] et al. [15] proposed a theme-aware social strength inference method to extract themes from spatiotemporal behavior data and analyzes the contribution of each theme of behavior to social strength.  
To analyze the social ties among large number of students over prolonged periods of time, [PERSON] [2] proposed an unsupervised statistical validation method based on the spatiotemporal behavior data on campus, taking spatiotemporal co-occurrence between two students as a measure to judge whether they have a tie, and validating ties against the null hypothesis that all co-occurrences are due to coincidence. They analyzed the structure of a network based on several metrics including degree distribution, degree assortativity, and attribute assortativity. Several differences exist between their work and our method, although both infer social ties from spatiotemporal behavior data. First, they only collected the check-in records in canteens and stores, while our behavior dataset also includes records of library entrance, book borrowing, and showering. Second, the community detection algorithms of the two methods are different. Our method uses the Louvain modularity optimization algorithm, while they used OSLOM. Third, the visualizations differ. We developed a visualization system, while they used Gephi to draw a force-directed graph.