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Composing activity groups in social networks

Authors:

Cheng-Te Li,

Man-Kwan ShanAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2375 - 2378

https://doi.org/10.1145/2396761.2398644

Published: 29 October 2012 Publication History

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Abstract

One important function of current social networking services is allowing users to initialize different kinds of activity groups (e.g. study group, cocktail party, and group buying) and invite friends to attend in either manual or collaborative manners. However, such process of group formation is tedious, and could either include inappropriate group members or miss relevant ones. This work proposes to automatically compose the activity groups in a social network according to user-specified activity information. Given the activity host, a set of labels representing the activity's subjects, the desired group size, and a set of must-inclusive persons, we aim to find a set of individuals as the activity group, in which members are required to not only be familiar with the host but also have great communications with each other. We devise an approximation algorithm to greedily solve the group composing problem. Experiments on a real social network show the promising effectiveness of the proposed approach as well as the satisfactory human subjective study.

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Cited By

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Alamri BMonowar MAlshehri S(2022)Privacy-Preserving Trust-Aware Group-Based Framework in Mobile CrowdsensingIEEE Access10.1109/ACCESS.2022.323240110(134770-134784)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3232401
Jiang JAn BJiang YZhang CBu ZCao J(2021)Group-Oriented Task Allocation for Crowdsourcing in Social NetworksIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2019.293332751:7(4417-4432)Online publication date: Jul-2021
https://doi.org/10.1109/TSMC.2019.2933327
Chen YYang DShen CLee WChen M(2019)On Efficient Processing of Group and Subsequent Queries for Social Activity PlanningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.287591131:12(2364-2378)Online publication date: 1-Dec-2019
https://doi.org/10.1109/TKDE.2018.2875911
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Index Terms

Composing activity groups in social networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Alamri BMonowar MAlshehri S(2022)Privacy-Preserving Trust-Aware Group-Based Framework in Mobile CrowdsensingIEEE Access10.1109/ACCESS.2022.323240110(134770-134784)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3232401
Jiang JAn BJiang YZhang CBu ZCao J(2021)Group-Oriented Task Allocation for Crowdsourcing in Social NetworksIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2019.293332751:7(4417-4432)Online publication date: Jul-2021
https://doi.org/10.1109/TSMC.2019.2933327
Chen YYang DShen CLee WChen M(2019)On Efficient Processing of Group and Subsequent Queries for Social Activity PlanningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.287591131:12(2364-2378)Online publication date: 1-Dec-2019
https://doi.org/10.1109/TKDE.2018.2875911
Yin ZXu TZhu HZhu CChen EXiong H(2019)Matching of social events and users: a two-way selection perspectiveWorld Wide Web10.1007/s11280-019-00724-7Online publication date: 27-Nov-2019
https://doi.org/10.1007/s11280-019-00724-7
Odo CMasthoff JBeacham N(2019)Group Formation for Collaborative LearningArtificial Intelligence in Education10.1007/978-3-030-23207-8_39(206-212)Online publication date: 21-Jun-2019
https://doi.org/10.1007/978-3-030-23207-8_39
Lafifi YZedadra AZedadra O(2018)Dynamic Group Formation based on a Natural PhenomenonInternational Journal of Distance Education Technologies10.4018/IJDET.201610010214:4(13-26)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.4018/IJDET.2016100102
Li CHuang MYan R(2018)Team formation with influence maximization for influential event organization on social networksWorld Wide Web10.1007/s11280-017-0492-721:4(939-959)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11280-017-0492-7
Ting LLi CChuang K(2018)Predictive Team Formation Analysis via Feature Representation Learning on Social NetworksAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-93040-4_62(790-802)Online publication date: 17-Jun-2018
https://doi.org/10.1007/978-3-319-93040-4_62
Liu LWang LSun HJia H(2018)Social recommendation between urban vehicular social communitiesInternational Journal of Communication Systems10.1002/dac.381031:17Online publication date: 19-Sep-2018
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Tang SSingh SMarkovitch S(2017)Profit-driven team grouping in social networksProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298247(45-51)Online publication date: 4-Feb-2017
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