article

Link mining applications: progress and challenges

Author:

Ted E. SenatorAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 7, Issue 2

Pages 76 - 83

https://doi.org/10.1145/1117454.1117465

Published: 01 December 2005 Publication History

Abstract

This article reviews a decade of progress in the area of link mining, focusing on application requirements and how they have and have not yet been addressed, especially in the area of complex event detection. It discusses some ongoing challenges and suggests ideas that could be opportunities for solutions. The most important conclusion of this article is that while there are many link mining techniques that work well for individual link mining tasks, there is not yet a comprehensive framework that can support a combination of link mining tasks as needed for many real applications.

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Link mining applications: progress and challenges

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Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 7, Issue 2

December 2005

152 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1117454

Issue’s Table of Contents

Copyright © 2005 Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2005

Published in SIGKDD Volume 7, Issue 2

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Cited By

Ghorbani ADaneshvar ARiazi LRadfar R(2022)Friend Recommender System for Social Networks Based on Stacking Technique and Evolutionary AlgorithmComplexity10.1155/2022/58645452022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5864545
Han ZLi SZheng CDuan DYang W(2020)Link prediction model based on dynamic network representationActa Physica Sinica10.7498/aps.69.2019116269:16(168901)Online publication date: 2020
https://doi.org/10.7498/aps.69.20191162
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https://doi.org/10.4000/books.pum.11733
Thuraisingham BAbrol SHeatherly RKantarcioglu MKhadilkar VKhan L(2016)Classification of Social Networks Incorporating Link TypesAnalyzing and Securing Social Networks10.1201/b19566-20(167-176)Online publication date: 31-Mar-2016
https://doi.org/10.1201/b19566-20
Il-Agure Z(2015)Anomalies in link mining based on mutual information2015 SAI Intelligent Systems Conference (IntelliSys)10.1109/IntelliSys.2015.7361276(1057-1059)Online publication date: Nov-2015
https://doi.org/10.1109/IntelliSys.2015.7361276
Creamer G(2015)Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model?Quantitative Finance10.1080/14697688.2015.103986515:8(1405-1416)Online publication date: 2-Jun-2015
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Sohail SSiddiqui JAli R(2014)Ordered ranked weighted aggregation based book recommendation technique: A link mining approach2014 14th International Conference on Hybrid Intelligent Systems10.1109/HIS.2014.7086167(309-314)Online publication date: Dec-2014
https://doi.org/10.1109/HIS.2014.7086167
Kumar M(2013)Applications of Data Mining in Dynamic Social Network AnalysisData Mining in Dynamic Social Networks and Fuzzy Systems10.4018/978-1-4666-4213-3.ch006(110-121)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-4213-3.ch006
Li PChen LLi XWen J(2013)RNRankInternational Workshop on Behavior and Social Informatics on Behavior and Social Computing - Volume 817810.1007/978-3-319-04048-6_13(139-150)Online publication date: 3-Aug-2013
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Creamer G(2013)Portfolio Optimization and Corporate Networks: Extending the Black Litterman ModelComplex Sciences10.1007/978-3-319-03473-7_8(83-94)Online publication date: 2013
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