Summarizing movement graph for mobility pattern analysis

Sadri, A, Ren, Y and Salim, F 2017, 'Summarizing movement graph for mobility pattern analysis', in Proceedings of the Knowledge Capture Conference (K-cap 2017), Austin, Texas, United States, December 04 - 06 2017, pp. 41-44.


Document type: Conference Paper
Collection: Conference Papers

Title Summarizing movement graph for mobility pattern analysis
Author(s) Sadri, A
Ren, Y
Salim, F
Year 2017
Conference name K-cap 2017 Knowledge Capture Conference
Conference location Austin, Texas, United States
Conference dates December 04 - 06 2017
Proceedings title Proceedings of the Knowledge Capture Conference (K-cap 2017)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 41
End page 44
Total pages 4
Abstract Understanding human mobility is the key problem in many applications such as location-based services and recommendation systems. The mobility of a smartphone user can be modeled by a movement graph, in which the nodes represent locations and the edges are distances or traveling times between the locations. However, the resulting graph would be too big to be stored and queried on resource-devices such as smartphones. In this paper, we deploy a state-of-the-art graph summarization method to produce an abstract (coarse) graph easy to be processed and queried. After summarization, the movement graph becomes smaller resulting in a reduction in the required time and storage to deploy graph algorithms. We specifically investigate the effect of summarization on two algorithms related to human mobility mining: location prediction and similarity mining. The location prediction algorithm on the coarse graph causes coarse-grain results. Regarding computing the similarity, summarization reduces the computational cost but at the same time increases the uncertainty of the results. We show that the trade-off between accuracy, storage space and speed can be controlled by the compression ratio. As an illustration, if the size of the graph is reduced to half, the similarity algorithm becomes 4 times faster while the correlation between similarities of coarse and original graphs is 0.98.
Subjects Pattern Recognition and Data Mining
DOI - identifier 10.1145/3148011.3154469
Copyright notice © 2017 Association for Computing Machinery
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