Recovering Social Networks From Massive Track Datasets

Abstract

Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we use the Mitsubishi track database to examine the usefulness of three types of interaction features observable in tracklet networks. We explore ways in which social network information can be extracted and visualized using a statistical sampling of these features from a very large track dataset, with very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

“…However, the most frequently linked sensors are neighborhood sensors, which suggests that people working in the office located at the sensor 272 mostly have working relations with the neighborhood office. This result can also be supported by similar analysis with the entropy method [ 15 ], which suggests the individuals associated with this sensor are frequently concentrated in the large office. From these statistical results, we believe that our method can be used to reveal the human motion patterns directly according to the sensor activation log data.…”

“…Other individual high frequency join statuses happen at sensors 214, 407, 441, 281, and 264; these sensors are located near the cross where people from different directions join their trajectories. In [ 15 ], sensor 442 is classified as the hub that connects different groups of people. In our result, the statements of [ 15 ] can also be partially supported.…”

“…In [ 15 ], sensor 442 is classified as the hub that connects different groups of people. In our result, the statements of [ 15 ] can also be partially supported. However, we can filter out more such kinds of hubs from the possible trajectories.…”

“…The temporal patterns of the human motion in the MERL dataset were analyzed by T-Pattern algorithm [ 13 , 14 ]. Research trying to recover the social networks from the spatio-temporal patterns of the interactions were also developed [ 15 , 16 ]. Because of the special characteristics of MERL dataset, it has been previously used in the IEEE Information Visualization Challenge, and presents a significant challenge for behavior analysis, search, manipulation and visualization.…”

Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science

“…For example, Ingram and Morris [2007] use such infrared badges to study patterns of interaction among executive MBA students at a mixer party. Similarly, Connolly et al [2008] use data collected from motion sensors [Wren et al 2007] to infer social events like walking together, attending the same meeting, or coincidentally meeting in a break room. Eagle and Pentland [2006] present a system for inferring physical proximity from the short-range Bluetooth radios in cell phones, and for inferring coarse absolute location using cell tower IDs.…”