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Information Diffusion at Workplace

Authors:

Qianyi ZhanAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 1673 - 1682

https://doi.org/10.1145/2983323.2983848

Published: 24 October 2016 Publication History

Abstract

People nowadays need to spend a large amount of time on their work everyday and workplace has become an important social occasion for effective communication and information exchange among employees. Besides traditional online contacts (e.g., face-to-face meetings and telephone calls), to facilitate the communication and cooperation among employees, a new type of online social networks has been launched inside the firewalls of many companies, which are named as the "enterprise social networks" (ESNs). In this paper, we want to study the information diffusion among employees at workplace via both online ESNs and online contacts. This is formally defined as the IDE (Information Diffusion in Enterprise) problem. Several challenges need to be addressed in solving the IDE problem: (1) diffusion channel extraction from online ESN and online contacts; (2) effective aggregation of the information delivered via different diffusion channels; and (3) communication channel weighting and selection. A novel information diffusion model, Muse (Multi-source Multi-channel Multi-topic diffUsion SElection), is introduced in this paper to resolve these challenges. Extensive experiments conducted on real-world ESN and organizational chart dataset demonstrate the outstanding performance of Muse in addressing the IDE problem.

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Cited By

Li HXia CWang TWen SChen CXiang Y(2021)Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and TechniquesACM Computing Surveys10.1145/348527355:1(1-51)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3485273
Zhang JYu PZhang JYu P(2019)Supervised Network AlignmentBroad Learning Through Fusions10.1007/978-3-030-12528-8_4(129-164)Online publication date: 9-Jun-2019
https://doi.org/10.1007/978-3-030-12528-8_4
Zhang JYu PZhang JYu P(2019)Frontier and Future DirectionsBroad Learning Through Fusions10.1007/978-3-030-12528-8_12(417-419)Online publication date: 9-Jun-2019
https://doi.org/10.1007/978-3-030-12528-8_12
Show More Cited By

Index Terms

Information Diffusion at Workplace
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

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

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Cited By

Li HXia CWang TWen SChen CXiang Y(2021)Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and TechniquesACM Computing Surveys10.1145/348527355:1(1-51)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3485273
Zhang JYu PZhang JYu P(2019)Supervised Network AlignmentBroad Learning Through Fusions10.1007/978-3-030-12528-8_4(129-164)Online publication date: 9-Jun-2019
https://doi.org/10.1007/978-3-030-12528-8_4
Zhang JYu PZhang JYu P(2019)Frontier and Future DirectionsBroad Learning Through Fusions10.1007/978-3-030-12528-8_12(417-419)Online publication date: 9-Jun-2019
https://doi.org/10.1007/978-3-030-12528-8_12
Zhang JYu P(2018)Broad Learning:ACM SIGKDD Explorations Newsletter10.1145/3229329.322933320:1(24-50)Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1145/3229329.3229333
Zhang JCui LYu PLv YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)BL-ECDProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133026(859-868)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133026
Zhang JChen JZhu JChang YYu Pde Rijke MShokouhi MTomkins AZhang M(2017)Link Prediction with Cardinality ConstraintProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018734(121-130)Online publication date: 2-Feb-2017
https://dl.acm.org/doi/10.1145/3018661.3018734
Zhang JYu PLv Yde Rijke MShokouhi MTomkins AZhang M(2017)Enterprise Employee Training via Project Team FormationProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018682(3-12)Online publication date: 2-Feb-2017
https://dl.acm.org/doi/10.1145/3018661.3018682
Shi CLi YZhang JSun YYu P(2017)A Survey of Heterogeneous Information Network AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.259856129:1(17-37)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2598561
Zhang JXia CZhang CCui LFu YYu P(2017)BL-MNE: Emerging Heterogeneous Social Network Embedding Through Broad Learning with Aligned Autoencoder2017 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2017.70(605-614)Online publication date: Nov-2017
https://doi.org/10.1109/ICDM.2017.70
Zhang JYu PLv Y(2017)Enterprise Community Detection2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.79(219-222)Online publication date: Apr-2017
https://doi.org/10.1109/ICDE.2017.79
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