On group popularity prediction in event-based social networks

Guangyu Li, Yong Liu, Bruno Ribeiro, Hao Ding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although previous work has shown that member and structural features are important to the future popularity of groups in EBSN, it is not yet clear how different member roles and the interplay between them contribute to group popularity. In this paper, we study a real-world dataset from Meetup - a popular EBSN platform - and propose a deep neural network based method to predict the popularity of new Meetup groups. Our method uses group-level features specific to event-based social networks, such as time and location of events in a group, as well as the structural features internal to a group, such as the inferred member roles in a group and social substructures among members. Empirically, our approach reduces the RMSE of the popularity prediction (measured in RSVPs) of a group;'s future events by up to 12%, against the state-of-the-art baselines.

Original languageEnglish (US)
Title of host publication12th International AAAI Conference on Web and Social Media, ICWSM 2018
PublisherAAAI press
Pages644-647
Number of pages4
ISBN (Electronic)9781577357988
StatePublished - Jan 1 2018
Event12th International AAAI Conference on Web and Social Media, ICWSM 2018 - Palo Alto, United States
Duration: Jun 25 2018Jun 28 2018

Publication series

Name12th International AAAI Conference on Web and Social Media, ICWSM 2018

Other

Other12th International AAAI Conference on Web and Social Media, ICWSM 2018
CountryUnited States
CityPalo Alto
Period6/25/186/28/18

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ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Li, G., Liu, Y., Ribeiro, B., & Ding, H. (2018). On group popularity prediction in event-based social networks. In 12th International AAAI Conference on Web and Social Media, ICWSM 2018 (pp. 644-647). (12th International AAAI Conference on Web and Social Media, ICWSM 2018). AAAI press.