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Tracing the Attention of Moving Citizens

View Article: PubMed Central - PubMed

ABSTRACT

With the widespread use of mobile computing devices in contemporary society, our trajectories in the physical space and virtual world are increasingly closely connected. Using the anonymous smartphone data of 1 × 105 users in a major city of China, we study the interplay between online and offline human behaviors by constructing the mobility network (offline) and the attention network (online). Using the network renormalization technique, we find that they belong to two different classes: the mobility network is small-world, whereas the attention network is fractal. We then divide the city into different areas based on the features of the mobility network discovered under renormalization. Interestingly, this spatial division manifests the location-based online behaviors, for example shopping, dating, and taxi-requesting. Finally, we offer a geometric network model to help us understand the relationship between small-world and fractal networks.

No MeSH data available.


Renormalization steps of the mobility network (A) and attention network (B). It takes 15 and 10 steps to tile the two networks, respectively.
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f1: Renormalization steps of the mobility network (A) and attention network (B). It takes 15 and 10 steps to tile the two networks, respectively.

Mentions: Song et al. revealed the fractal property of complex networks using the box-counting method7. We apply the same renormalization technique in both the mobility network and the attention network. Figure 1 shows the renormalization steps for two networks. There are 9,899 nodes and 39,083 edges in the mobility network, and 16,476 nodes and 144,909 edges in the attention network. The diameter of the mobility network and the attention network is 15 and 10, respectively. The length of diameter also indicates the steps needed to collapse the networks into a single node.


Tracing the Attention of Moving Citizens
Renormalization steps of the mobility network (A) and attention network (B). It takes 15 and 10 steps to tile the two networks, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016808&req=5

f1: Renormalization steps of the mobility network (A) and attention network (B). It takes 15 and 10 steps to tile the two networks, respectively.
Mentions: Song et al. revealed the fractal property of complex networks using the box-counting method7. We apply the same renormalization technique in both the mobility network and the attention network. Figure 1 shows the renormalization steps for two networks. There are 9,899 nodes and 39,083 edges in the mobility network, and 16,476 nodes and 144,909 edges in the attention network. The diameter of the mobility network and the attention network is 15 and 10, respectively. The length of diameter also indicates the steps needed to collapse the networks into a single node.

View Article: PubMed Central - PubMed

ABSTRACT

With the widespread use of mobile computing devices in contemporary society, our trajectories in the physical space and virtual world are increasingly closely connected. Using the anonymous smartphone data of 1 × 105 users in a major city of China, we study the interplay between online and offline human behaviors by constructing the mobility network (offline) and the attention network (online). Using the network renormalization technique, we find that they belong to two different classes: the mobility network is small-world, whereas the attention network is fractal. We then divide the city into different areas based on the features of the mobility network discovered under renormalization. Interestingly, this spatial division manifests the location-based online behaviors, for example shopping, dating, and taxi-requesting. Finally, we offer a geometric network model to help us understand the relationship between small-world and fractal networks.

No MeSH data available.