Lin Pan | Tianjin University (original) (raw)
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Papers by Lin Pan
Physica A: Statistical Mechanics and its Applications, 2015
h i g h l i g h t s
Quantitative understanding of human movement behaviors would provide helpful insights into the me... more Quantitative understanding of human movement behaviors would provide helpful insights into the mechanisms of many socioeconomic phenomena. In this paper, we investigate hu- man mobility patterns through analyzing taxi-trace datasets collected from five metropoli- tan cities in two countries. We focus on three statistics for each dataset: the displacement of each occupied trip, the duration of each occupied trip, and the time interval between successive occupied trips by the same taxi (interevent time). The results indicate that the displacement distributions of human travel by taxi tend to follow exponential laws in two displacement ranges rather than power laws; the trip duration distributions can be approx- imated by log-normal distributions; the interevent time distributions can be well charac- terized by log-normal bodies followed by power law tails. For each considered measure, the rescaled distributions of all cities collapsed into a master curve. These results provide em- pirical evidence supporting the common regularity of intra-city human mobility. Moreover, we show that airport locations could play a role in explaining the spikes of displacement distributions of taxi trips in certain cities.
A fuzzy overlapping community is an important kind of overlapping community in which each node be... more A fuzzy overlapping community is an important kind of overlapping community in which each node belongs to each community to different extents. It exists in many real networks but how to identify a fuzzy overlapping community is still a challenging task. In this work, the concept of local random walk and a new distance metric are introduced. Based on the new distance measurement, the dissimilarity index between each node of a network is calculated firstly. Then in order to keep the original node distance as much as possible, the network structure is mapped into low-dimensional space by the multidimensional scaling (MDS). Finally, the fuzzy c-means clustering is employed to find fuzzy communities in a network. The experimental results show that the proposed algorithm is effective and efficient to identify the fuzzy overlapping communities in both artificial networks and real- world networks.
Physica A: Statistical Mechanics and its Applications, 2015
h i g h l i g h t s
Quantitative understanding of human movement behaviors would provide helpful insights into the me... more Quantitative understanding of human movement behaviors would provide helpful insights into the mechanisms of many socioeconomic phenomena. In this paper, we investigate hu- man mobility patterns through analyzing taxi-trace datasets collected from five metropoli- tan cities in two countries. We focus on three statistics for each dataset: the displacement of each occupied trip, the duration of each occupied trip, and the time interval between successive occupied trips by the same taxi (interevent time). The results indicate that the displacement distributions of human travel by taxi tend to follow exponential laws in two displacement ranges rather than power laws; the trip duration distributions can be approx- imated by log-normal distributions; the interevent time distributions can be well charac- terized by log-normal bodies followed by power law tails. For each considered measure, the rescaled distributions of all cities collapsed into a master curve. These results provide em- pirical evidence supporting the common regularity of intra-city human mobility. Moreover, we show that airport locations could play a role in explaining the spikes of displacement distributions of taxi trips in certain cities.
A fuzzy overlapping community is an important kind of overlapping community in which each node be... more A fuzzy overlapping community is an important kind of overlapping community in which each node belongs to each community to different extents. It exists in many real networks but how to identify a fuzzy overlapping community is still a challenging task. In this work, the concept of local random walk and a new distance metric are introduced. Based on the new distance measurement, the dissimilarity index between each node of a network is calculated firstly. Then in order to keep the original node distance as much as possible, the network structure is mapped into low-dimensional space by the multidimensional scaling (MDS). Finally, the fuzzy c-means clustering is employed to find fuzzy communities in a network. The experimental results show that the proposed algorithm is effective and efficient to identify the fuzzy overlapping communities in both artificial networks and real- world networks.