Abstract
In geo-social networks, the distances of users to a location play an important role in populating the business or campaign at the location. Thereby, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM computation heavily relies on the sample location selection, because the online seeding performance is sensitive to the distance between sample location and promoted location, and the offline precomputation performance is sensitive to the number of samples. However, there is no work to fully study the problem of sample location selection w.r.t. DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users. Then, we propose an efficient location sampling approach based on the heuristic anchor point selection and facility allocation techniques. Our experimental results on two real datasets demonstrate that our approach can improve the online and offline efficiency of DAIM approach like [9] by orders of magnitude.
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Change history
01 July 2018
The original version of this chapter titled “Free-Rider Episode Screening via Dual Partition Model” contained the following three mistakes:
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In table 1, row 2, column 3, the average occurrence per event on STK dataset was “1.037”. It should be “1,037”.
- 2.
The last model name in the legend of Figure 3 was “EIP”. It should be “EDP”.
- 3.
In the experiment part the stock symbols and their companies were confused.
In the updated version these mistakes were corrected.
In the originally published version of chapters titled “BASSI: Balance and Status Combined Signed Network Embedding” and “Sample Location Selection for Efficient Distance-Aware Influence Maximization in Geo-Social Networks” the funding information in the acknowledgement section was incomplete. This has now been corrected.
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Notes
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The 2D space in this example is the surface of earth. In reality, we only consider a city or even smaller district for location promotion. However, the situation of sparse user distribution remains the same.
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Acknowledgement
This paper was supported by National Natural Science Foundation of China under Grant No. 61202036, 61502349 and 61572376 and Natural Science Foundation of Hubei Province under Grant No. 2018CFB616.
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Zhong, M., Zeng, Q., Zhu, Y., Li, J., Qian, T. (2018). Sample Location Selection for Efficient Distance-Aware Influence Maximization in Geo-Social Networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_24
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