More Web Proxy on the site http://driver.im/

Article

Why collective inference improves relational classification

Authors:

Jennifer Neville,

Brian GallagherAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 593 - 598

https://doi.org/10.1145/1014052.1014125

Published: 22 August 2004 Publication History

Abstract

Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could be used to simultaneously classify a set of hyperlinked documents or infer the legitimacy of a set of related financial transactions. Several recent studies indicate that collective inference can significantly reduce classification error when compared with traditional inference techniques. We investigate the underlying mechanisms for this error reduction by reviewing past work on collective inference and characterizing different types of statistical models used for making inference in relational data. We show important differences among these models, and we characterize the necessary and sufficient conditions for reduced classification error based on experiments with real and simulated data.

References

[1]

Chakrabarti, S., B. Dom & P. Indyk. Enhanced Hypertext Classification Using Hyper-Links, In Proc. ACM SIGMOD Conference, pp. 307--318, 1998.]]

Digital Library

[2]

Domingos, P. A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. In Proc. of the 17th National Conference on Artificial Intelligence, pp. 564--569, 2000.]]

Digital Library

[3]

Domingos, P. & M. Richardson. Mining the Network Value of Customers. In Proc. of the 7th International Conference on Knowledge Discovery and Data Mining, pp. 57--66, 2001.]]

Digital Library

[4]

Getoor, L., N. Friedman, D. Koller, & A. Pfeffer. Learning Probabilistic Relational Models. In Relational Data Mining, S. Dzeroski and N. Lavrac, Eds., Springer-Verlag, 2001.]]

Digital Library

[5]

Getoor, L., E. Segal, B. Taskar, & D. Koller. Probabilistic Models of Text and Link Structure for Hypertext Classification. In Proc. IJCAI01 Workshop on Text Learning: Beyond Supervision, 2001.]]

[6]

Getoor, L., J. Rhee, D. Koller, & P. Small. Understanding Tuberculosis Epidemiology using Probabilistic Relational Models. Journal of Artificial Intelligence in Medicine, vol. 30, pp. 233--256, 2004.]]

Digital Library

[7]

Jensen, D. & J. Neville. Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. In Proc. of the 19th International Conference on Machine Learning, pp. 259--266, 2002.]]

Digital Library

[8]

Kersting, K. & L. De Raedt. Basic principles of learning Bayesian logic programs. Technical Report No. 174, Institute for Computer Science, University of Freiburg, Germany, June 2002.]]

[9]

Macskassy, S. & F. Provost. A Simple Relational Classifier. In Proc. KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 64--76, 2003.]]

[10]

Neville, J. & D. Jensen. Iterative Classification in Relational Data. In Proc. AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pp. 13--20, 2000.]]

[11]

Neville, J. & D. Jensen. Supporting Relational Knowledge Discovery: Lessons in Architecture and Algorithm Design. In Proc. ICML2002 Data Mining Lessons Learned Workshop, pp. 57--64, 2002.]]

[12]

Neville, J., & Jensen, D. Collective Classification with Relational Dependency Networks. In Proc. KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 77--91, 2003.]]

[13]

Neville, J., D. Jensen & B. Gallagher. Simple Estimators for Relational Bayesian Classifiers. In Proc. of the 3rd IEEE International Conference on Data Mining, pp. 609--612, 2003.]]

Digital Library

[14]

Slattery, S., & T. Mitchell. Discovering Test Set Regularities in Relational Domains. In Proc. 17th International Conference on Machine Learning, pp.895--902, 2000.]]

Digital Library

[15]

Taskar, B., P. Abbeel & D. Koller. Discriminative Probabilistic Models for Relational Data. In Proc. 18th Conference on Uncertainty in Artificial Intelligence, pp. 485--492, 2002.]]

Digital Library

[16]

Taskar, B., E. Segal & D. Koller. Probabilistic Classification and Clustering in Relational Data. In Proc. 17th International Joint Conference on Artificial Intelligence, pp. 870--878, 2001.]]

Digital Library

[17]

Yang, Y, S. Slattery & R. Ghani. A Study of Approaches to Hypertext Categorization. Journal of Intelligent Information Systems. 18(2-3): 219--241. 2002.]]

Digital Library

Cited By

Shen JTian JWang Z(2024)Privacy-preserving algorithm based on vulnerable nodes for social relationshipsThe Journal of Supercomputing10.1007/s11227-024-06308-180:15(22654-22681)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s11227-024-06308-1
Corizzo RPio GBarracchia EPellicani AJapkowicz NCeci M(2023)HURI: Hybrid user risk identification in social networksWorld Wide Web10.1007/s11280-023-01192-w26:5(3409-3439)Online publication date: 28-Jul-2023
https://doi.org/10.1007/s11280-023-01192-w
Vijayan PChandak YKhapra MParthasarathy SRavindran B(2022)Scaling Graph Propagation Kernels for Predictive LearningFrontiers in Big Data10.3389/fdata.2022.6166175Online publication date: 8-Apr-2022
https://doi.org/10.3389/fdata.2022.616617
Show More Cited By

Index Terms

Why collective inference improves relational classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

Recommendations

Collective inference for network data with copula latent markov networks
WSDM '13: Proceedings of the sixth ACM international conference on Web search and data mining

The popularity of online social networks and social media has increased the amount of linked data available in Web domains. Relational and Gaussian Markov networks have both been applied successfully for classification in these relational settings. ...
A bias/variance decomposition for models using collective inference

Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the learning process, but in relational domains, the inference process used for ...
Budgeted online collective inference
UAI'15: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence

Updating inference in response to new evidence is a fundamental challenge in artificial intelligence. Many real problems require large probabilistic graphical models, containing millions of interdependent variables. For such large models, jointly ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 ACM.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

KDD04

Sponsor:

KDD04: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 22 - 25, 2004

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

168
Total Citations
View Citations
996
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)3

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Shen JTian JWang Z(2024)Privacy-preserving algorithm based on vulnerable nodes for social relationshipsThe Journal of Supercomputing10.1007/s11227-024-06308-180:15(22654-22681)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s11227-024-06308-1
Corizzo RPio GBarracchia EPellicani AJapkowicz NCeci M(2023)HURI: Hybrid user risk identification in social networksWorld Wide Web10.1007/s11280-023-01192-w26:5(3409-3439)Online publication date: 28-Jul-2023
https://doi.org/10.1007/s11280-023-01192-w
Vijayan PChandak YKhapra MParthasarathy SRavindran B(2022)Scaling Graph Propagation Kernels for Predictive LearningFrontiers in Big Data10.3389/fdata.2022.6166175Online publication date: 8-Apr-2022
https://doi.org/10.3389/fdata.2022.616617
Hosseini MHeaps JSlavin RNiu JBreaux T(2021)Ambiguity and Generality in Natural Language Privacy Policies2021 IEEE 29th International Requirements Engineering Conference (RE)10.1109/RE51729.2021.00014(70-81)Online publication date: Sep-2021
https://doi.org/10.1109/RE51729.2021.00014
Espín-Noboa LKarimi FRibeiro BLerman KWagner C(2021)Explaining classification performance and bias via network structure and sampling techniqueApplied Network Science10.1007/s41109-021-00394-36:1Online publication date: 21-Oct-2021
https://doi.org/10.1007/s41109-021-00394-3
Zhu ZFan XChu XBi JGupta RLiu YTang JPrakash B(2020)HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective ClassificationProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403169(1161-1171)Online publication date: 20-Aug-2020
https://doi.org/10.1145/3394486.3403169
Abbasi RKashif Bashir AChen JMateen APiran JAmin FLuo B(2020)Author classification using transfer learning and predicting stars in co‐author networksSoftware: Practice and Experience10.1002/spe.288451:3(645-669)Online publication date: 28-Sep-2020
https://doi.org/10.1002/spe.2884
Park HNeville J(2019)Exploiting interaction links for node classification with deep graph neural networksProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367489(3223-3230)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367489
De ABhattacharya SBhattacharya PGanguly NChakrabarti S(2019)Learning Linear Influence Models in Social Networks from Transient Opinion DynamicsACM Transactions on the Web10.1145/334348313:3(1-33)Online publication date: 11-Nov-2019
https://dl.acm.org/doi/10.1145/3343483
Xiong YZhang YKong XChen HZhu Y(2019)GraphInception: Convolutional Neural Networks for Collective Classification in Heterogeneous Information NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2947458(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2947458
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents