research-article

Learning to Infer Competitive Relationships in Heterogeneous Networks

Authors:

Juanzi LiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 1

Article No.: 12, Pages 1 - 23

https://doi.org/10.1145/3051127

Published: 13 February 2018 Publication History

Abstract

Detecting and monitoring competitors is fundamental to a company to stay ahead in the global market. Existing studies mainly focus on mining competitive relationships within a single data source, while competing information is usually distributed in multiple networks. How to discover the underlying patterns and utilize the heterogeneous knowledge to avoid biased aspects in this issue is a challenging problem. In this article, we study the problem of mining competitive relationships by learning across heterogeneous networks. We use Twitter and patent records as our data sources and statistically study the patterns behind the competitive relationships. We find that the two networks exhibit different but complementary patterns of competitions. Overall, we find that similar entities tend to be competitors, with a probability of 4 times higher than chance. On the other hand, in social network, we also find a 10 minutes phenomenon: when two entities are mentioned by the same user within 10 minutes, the likelihood of them being competitors is 25 times higher than chance. Based on the discovered patterns, we propose a novel Topical Factor Graph Model. Generally, our model defines a latent topic layer to bridge the Twitter network and patent network. It then employs a semi-supervised learning algorithm to classify the relationships between entities (e.g., companies or products). We test the proposed model on two real data sets and the experimental results validate the effectiveness of our model, with an average of +46% improvement over alternative methods. Besides, we further demonstrate the competitive relationships inferred by our proposed model can be applied in the job-hopping prediction problem by achieving an average of +10.7% improvement.

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Cited By

Ye XSun YLiu DLi T(2023)A Multisource Data Fusion-based Heterogeneous Graph Attention Network for Competitor PredictionACM Transactions on Knowledge Discovery from Data10.1145/362510118:2(1-20)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3625101
Liu YWu HHuang ZWang HNing YMa JLiu QChen E(2023)TechPat: Technical Phrase Extraction for Patent MiningACM Transactions on Knowledge Discovery from Data10.1145/359660317:9(1-31)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3596603
Sun ZLi CLei YZhang LZhang JLiang S(2022)Point-of-Interest Recommendation for Users-Businesses With Uncertain Check-insIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306081834:12(5925-5938)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3060818
Show More Cited By

Index Terms

Learning to Infer Competitive Relationships in Heterogeneous Networks
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
      1. Social networks

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 12, Issue 1

Special Issue (IDEA) and Regular Papers

February 2018

363 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3178542

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2018

Accepted: 01 February 2017

Revised: 01 July 2016

Received: 01 September 2015

Published in TKDD Volume 12, Issue 1

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Natural Science Foundation of China
Royal Society-Newton Advanced Fellowship Award
MSRA
Chinese National Key Foundation Research

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Cited By

Ye XSun YLiu DLi T(2023)A Multisource Data Fusion-based Heterogeneous Graph Attention Network for Competitor PredictionACM Transactions on Knowledge Discovery from Data10.1145/362510118:2(1-20)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3625101
Liu YWu HHuang ZWang HNing YMa JLiu QChen E(2023)TechPat: Technical Phrase Extraction for Patent MiningACM Transactions on Knowledge Discovery from Data10.1145/359660317:9(1-31)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3596603
Sun ZLi CLei YZhang LZhang JLiang S(2022)Point-of-Interest Recommendation for Users-Businesses With Uncertain Check-insIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306081834:12(5925-5938)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3060818
Werle MLaumer S(2022)Competitor identificationInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2022.10250765:COnline publication date: 15-Jun-2022
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2022.102507
Yang YXu YSun YDong YWu FZhuang Y(2021)Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.292443133:1(169-179)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TKDE.2019.2924431
Guo BLiu YOuyang YZheng VZhang DYu Z(2019)Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A SurveyIEEE Access10.1109/ACCESS.2019.2901027(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2901027
Zhang HGong JTeng ZWang DWang HDu LBhuiyan Z(2019)Mobile APP User Attribute Prediction by Heterogeneous Information Network ModelingDependability in Sensor, Cloud, and Big Data Systems and Applications10.1007/978-981-15-1304-6_23(294-303)Online publication date: 5-Nov-2019
https://doi.org/10.1007/978-981-15-1304-6_23

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