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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.


Geometric network models.The first column shows the model proposed by Zhang et al. (Model-all)13, in which a new node is connected to all nodes within a fixed radius r. The second and the third columns show the other two versions of models, in which a new node is connected to the highest degree node (Model-max) and the lowest degree node (Model-min), respectively. The first row shows the global structure of networks and the second row shows a local part of the networks. The parameters of the model are set as follows: the size of 2D space = 100 × 100, the radius = 3, the number of simulation time steps = 10,000. The third row compares the dynamics of different models and shows that Model-min and Model-max replicates the patterns observed in the mobility network (positive degree correlation and exponential relation between N(lB) and lB) and in the attention network (negative degree correlation and power-law relation between N(lB) and lB), respectively.
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f5: Geometric network models.The first column shows the model proposed by Zhang et al. (Model-all)13, in which a new node is connected to all nodes within a fixed radius r. The second and the third columns show the other two versions of models, in which a new node is connected to the highest degree node (Model-max) and the lowest degree node (Model-min), respectively. The first row shows the global structure of networks and the second row shows a local part of the networks. The parameters of the model are set as follows: the size of 2D space = 100 × 100, the radius = 3, the number of simulation time steps = 10,000. The third row compares the dynamics of different models and shows that Model-min and Model-max replicates the patterns observed in the mobility network (positive degree correlation and exponential relation between N(lB) and lB) and in the attention network (negative degree correlation and power-law relation between N(lB) and lB), respectively.

Mentions: We find that Model-min shows positive degree correlation and Model-max displays negative degree correlation (Fig. 5H). Meanwhile, the number of box N(lB) is a power-law function of the length of box lB in Model-max, whereas it is an exponential function of lB in Model-min (Fig. 5G). Therefore, Model-min and Model-max replicate the patterns observed in the mobility network and in the attention network, respectively.


Tracing the Attention of Moving Citizens
Geometric network models.The first column shows the model proposed by Zhang et al. (Model-all)13, in which a new node is connected to all nodes within a fixed radius r. The second and the third columns show the other two versions of models, in which a new node is connected to the highest degree node (Model-max) and the lowest degree node (Model-min), respectively. The first row shows the global structure of networks and the second row shows a local part of the networks. The parameters of the model are set as follows: the size of 2D space = 100 × 100, the radius = 3, the number of simulation time steps = 10,000. The third row compares the dynamics of different models and shows that Model-min and Model-max replicates the patterns observed in the mobility network (positive degree correlation and exponential relation between N(lB) and lB) and in the attention network (negative degree correlation and power-law relation between N(lB) and lB), respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Geometric network models.The first column shows the model proposed by Zhang et al. (Model-all)13, in which a new node is connected to all nodes within a fixed radius r. The second and the third columns show the other two versions of models, in which a new node is connected to the highest degree node (Model-max) and the lowest degree node (Model-min), respectively. The first row shows the global structure of networks and the second row shows a local part of the networks. The parameters of the model are set as follows: the size of 2D space = 100 × 100, the radius = 3, the number of simulation time steps = 10,000. The third row compares the dynamics of different models and shows that Model-min and Model-max replicates the patterns observed in the mobility network (positive degree correlation and exponential relation between N(lB) and lB) and in the attention network (negative degree correlation and power-law relation between N(lB) and lB), respectively.
Mentions: We find that Model-min shows positive degree correlation and Model-max displays negative degree correlation (Fig. 5H). Meanwhile, the number of box N(lB) is a power-law function of the length of box lB in Model-max, whereas it is an exponential function of lB in Model-min (Fig. 5G). Therefore, Model-min and Model-max replicate the patterns observed in the mobility network and in the attention network, respectively.

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.