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Mining competitive relationships by learning across heterogeneous networks

Published: 29 October 2012 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 paper, 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. Our proposed model, Topical Factor Graph Model (TFGM), defines a latent topic layer to bridge the two networks and learns a semi-supervised learning model 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.

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      cover image ACM Conferences
      CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
      October 2012
      2840 pages
      ISBN:9781450311564
      DOI:10.1145/2396761
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      Published: 29 October 2012

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      Author Tags

      1. competitive relationship
      2. social network
      3. web mining

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      • (2024)Knowledge‐driven spatial competitive intelligence for tourismTransactions in GIS10.1111/tgis.1314528:3(535-563)Online publication date: 25-Feb-2024
      • (2024)CR-LCRPExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123777249:PCOnline publication date: 17-Jul-2024
      • (2022)Points-of-interest relationship inference with spatial-enriched graph neural networksProceedings of the VLDB Endowment10.14778/3494124.349413415:3(504-512)Online publication date: 4-Feb-2022
      • (2022)Competitive Relationship Prediction for Points of Interest: A Neural Graphlet Based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306323334:12(5681-5692)Online publication date: 1-Dec-2022
      • (2021)Explainable Recommendation with Comparative Constraints on Product AspectsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441754(967-975)Online publication date: 8-Mar-2021
      • (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
      • (2020)Competitive Analysis for Points of InterestProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403179(1265-1274)Online publication date: 23-Aug-2020
      • (2020)Large-Scale Talent Flow Embedding for Company Competitive AnalysisProceedings of The Web Conference 202010.1145/3366423.3380299(2354-2364)Online publication date: 20-Apr-2020
      • (2020)Competitive Analysis with Graph Embedding on Patent Networks2020 IEEE 22nd Conference on Business Informatics (CBI)10.1109/CBI49978.2020.00009(10-19)Online publication date: Jun-2020
      • (2019)Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A SurveyIEEE Access10.1109/ACCESS.2019.29010277(26606-26630)Online publication date: 2019
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