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

Likhyani, Ankita and Gupta, Vinayak and Srijith, P K and P., Deepak and Bedathur, Srikanta (2020) Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes. In: 21st International Conference on Web Information Systems Engineering, WISE 2020, 20-24 October 2020, Amsterdam.

Full text not available from this repository. (Request a copy)

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. © 2020, Springer Nature Switzerland AG.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Srijith, P K0000-0002-2820-0835
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Community detection; Information diffusion; Location based social networks; Spatio-temporal point process
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 15 Nov 2022 06:06
Last Modified: 15 Nov 2022 06:06
URI: http://raiithold.iith.ac.in/id/eprint/11243
Publisher URL: http://doi.org/10.1007/978-3-030-62005-9_12
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 11243 Statistics for this ePrint Item