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Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes

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Web Information Systems Engineering – WISE 2020 (WISE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12342))

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Abstract

The location check-ins of users through various location-based services such as Foursquare, Twitter and Facebook Places, generate large traces of geo-tagged events. These event-traces often manifest in hidden (possibly overlapping) communities of users with similar interests. Inferring these implicit communities is crucial for forming user profiles for improvements in recommendation and prediction tasks. Given only time-stamped geo-tagged traces of users, can we find out these implicit communities, and characteristics of the underlying influence network? Can we use this network to improve the next location prediction task? In this paper, we focus on the problem of community detection as well as capturing the underlying diffusion process. We propose CoLAB, based on spatio-temporal point processes for information diffusion in continuous time but discrete space of locations. It simultaneously models the implicit communities of users based on their check-in activities, without making use of their social network connections. CoLAB captures the semantic features of the location, user-to-user influence along with spatial and temporal preferences of users. The latent community of users and model parameters are learnt through stochastic variational inference. To the best of our knowledge, this is the first attempt at jointly modeling the diffusion process with activity-driven implicit communities. We demonstrate CoLAB achieves upto 27% improvements in location prediction task over recent deep point-process based methods on geo-tagged event traces collected from Foursquare check-ins.

A. Likhyani and V. Gupta are contributed equally.

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Change history

  • 18 October 2020

    In Chapter 24, a co-author listed on the Consent to Publish form was inadvertently forgotten. This mistake has been corrected and the forgotten co-author has been added.

Notes

  1. 1.

    https://bit.ly/2BdhnnP (accessed in February 2019).

  2. 2.

    We overload the notation c to also represent a scalar categorical value in the set \(\{ 1,\ldots , V\}\).

  3. 3.

    https://en.wikipedia.org/wiki/Silhouette_(clustering).

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Author information

Authors and Affiliations

  1. Indraprastha Institute of Information Technology Delhi, Delhi, India

    Ankita Likhyani

  2. Indian Institute of Technology Delhi, Delhi, India

    Vinayak Gupta & Srikanta Bedathur

  3. Indian Institute of Technology Hyderabad, Hyderabad, India

    P. K. Srijith

  4. Queen’s University Belfast, Belfast, UK

    Deepak P.

Authors
  1. Ankita Likhyani
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  2. Vinayak Gupta
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  3. P. K. Srijith
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  4. Deepak P.
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  5. Srikanta Bedathur
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Corresponding author

Correspondence to Ankita Likhyani .

Editor information

Editors and Affiliations

  1. VU Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  2. VU Amsterdam, Amsterdam, The Netherlands

    Wouter Beek

  3. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Melbourne, VIC, Australia

    Yanchun Zhang

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Likhyani, A., Gupta, V., Srijith, P.K., P., D., Bedathur, S. (2020). Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-62005-9_12

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