demonstration

Research-insight: providing insight on research by publication network analysis

Authors:

Xiao Yu,

Tim WeningerAuthors Info & Claims

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Pages 1093 - 1096

https://doi.org/10.1145/2463676.2463689

Published: 22 June 2013 Publication History

Get Access

Abstract

A database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, intercon- nected, heterogeneous information networks. Much knowl- edge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this system demo, we take a computer science research publica- tion network as an example, which is an information net- work derived from an integration of DBLP, other web-based information about researchers, and partially available cita- tion data, and construct a Research-Insight system in order to demonstrate the power of database-oriented information network analysis. We show that nontrivial research insight can be obtained from such analysis, including (1) ranking, clustering, classification and similarity search of researchers, terms and venues for research subfields and themes, (2) recommending good researchers and good research papers to read or cite when conducting research on certain topics (3) predicting potential collaborators for certain theme-oriented research, and (4) predicting advisor-advisee rela- tionships and affiliation history based on historical research publications. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses challenging research problems. Moreover, some function are our on- going research tasks. By integrating these functionalities, Research-Insight may not only provide with us insightful rec- ommendations in CS research but also help us gain insight on how to perform effective data mining in large databases.

References

[1]

M. Ji, J. Han, and M. Danilevsky. Ranking-based classification of heterogeneous information networks. In Proc. 2011 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'11), San Diego, CA, Aug. 2011.

Digital Library

Google Scholar

[2]

M. Ji, Y. Sun, M. Danilevsky, J. Han, and J. Gao. Graph regularized transductive classification on heterogeneous information networks. In Proc. 2010 European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'10), Barcelona, Spain, Sept. 2010.

Digital Library

Google Scholar

[3]

Y. Sun, R. Barber, M. Gupta, C. Aggarwal, and J. Han. Co-author relationship prediction in heterogeneous bibliographic networks. In Proc. 2011 Int. Conf. Advances in Social Network Analysis and Mining (ASONAM'11), Kaohsiung, Taiwan, July 2011.

Digital Library

Google Scholar

[4]

Y. Sun and J. Han. Mining Heterogeneous Information Networks: Principles and Methodologies. Morgan & Claypool Publishers, 2012.

Digital Library

Google Scholar

[5]

Y. Sun, J. Han, C. C. Aggarwal, and N. Chawla. When will it happen? relationship prediction in heterogeneous information networks. In Proc. 2012 ACM Int. Conf. on Web Search and Data Mining (WSDM'12), Seattle, WA, Feb. 2012.

Digital Library

Google Scholar

[6]

Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu. PathSim: Meta path-based top-k similarity search in heterogeneous information networks. In Proc. 2011 Int. Conf. Very Large Data Bases (VLDB'11), Seattle, WA, Aug. 2011.

Digital Library

Google Scholar

[7]

Y. Sun, J. Han, P. Zhao, Z. Yin, H. Cheng, and T. Wu. RankClus: Integrating clustering with ranking for heterogeneous information network analysis. In Proc. 2009 Int. Conf. Extending Data Base Technology (EDBT'09), Saint-Petersburg, Russia, Mar. 2009.

Digital Library

Google Scholar

[8]

Y. Sun, Y. Yu, and J. Han. Ranking-based clustering of heterogeneous information networks with star network schema. In Proc. 2009 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD'09), Paris, France, June 2009.

Digital Library

Google Scholar

[9]

C. Wang, J. Han, Y. Jia, J. Tang, D. Zhang, Y. Yu, and J. Guo. Mining advisor-advisee relationships from research publication networks. In Proc. 2010 ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD'10), Washington D.C., July 2010.

Digital Library

Google Scholar

[10]

T. Weninger, F. Fumarola, C. Xide Lin, R. Barber, J. Han, and D. Malerba. Growing parallel paths for entity-page discovery. In Proc. 2011 Int. World Wide Web Conf. (WWW'11), Hyderabad, India, Mar. 2011.

Digital Library

Google Scholar

[11]

X. Yu, Q. Gu, M. Zhou, and J. Han. Citation prediction in heterogeneous bibliographic networks. In Proc. 2012 SIAM Int. Conf. on Data Mining (SDM'12), Anaheim, CA, April 2012.

Crossref

Google Scholar

Cited By

View all

Patel HParaskevopoulos PRenz M(2018)Data Fusion of Diverse Data SourcesProceedings of the Fifth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3210272.3210275(13-18)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3210272.3210275
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
Shi CYu PShi CYu P(2017)Prototype System Based on Heterogeneous NetworkHeterogeneous Information Network Analysis and Applications10.1007/978-3-319-56212-4_8(201-217)Online publication date: 26-May-2017
https://doi.org/10.1007/978-3-319-56212-4_8
Show More Cited By

Index Terms

Research-insight: providing insight on research by publication network analysis
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Sparse representation for heterogeneous information networks
Highlights
- A sparse representation method of heterogeneous information networks is proposed.
- The heterogeneous information atoms are found by the sparse representation method.
- The heterogeneous information atoms describe the main patterns of ...
Abstract
A complex network is a fundamental tool to describe real-world complex systems, with most real-world systems containing multiple object types and relationships that can be described as heterogeneous information networks. However, with the ...
Mining Query-Based Subnetwork Outliers in Heterogeneous Information Networks
ICDM '14: Proceedings of the 2014 IEEE International Conference on Data Mining

Mining outliers in a heterogeneous information network is a challenging problem: It is even unclear what should be outliers in a large heterogeneous network (e.g., Outliers in the entire bibliographic network consisting of authors, titles, papers and ...
Iceberg Cube Query on Heterogeneous Information Networks
WASA 2014: Proceedings of the 9th International Conference on Wireless Algorithms, Systems, and Applications - Volume 8491

With the rapid development of social networks and cyber-physical systems, heterogeneous information networks have become increasingly popular. In many cases, such networks contain multiple types of objects and links. In traditional data warehouses and ...

Comments

Information & Contributors

Information

Published In

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

June 2013

1322 pages

ISBN:9781450320375

DOI:10.1145/2463676

General Chairs:
Kenneth Ross
Columbia University
,
Divesh Srivastava
AT&T Research
,
Program Chair:
Dimitris Papadias
HKUST

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

SIGMOD/PODS'13

Sponsor:

SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 22 - 27, 2013

New York, New York, USA

Acceptance Rates

SIGMOD '13 Paper Acceptance Rate 76 of 372 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
306
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Patel HParaskevopoulos PRenz M(2018)Data Fusion of Diverse Data SourcesProceedings of the Fifth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3210272.3210275(13-18)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3210272.3210275
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
Shi CYu PShi CYu P(2017)Prototype System Based on Heterogeneous NetworkHeterogeneous Information Network Analysis and Applications10.1007/978-3-319-56212-4_8(201-217)Online publication date: 26-May-2017
https://doi.org/10.1007/978-3-319-56212-4_8
Tao FBrova GHan JJi HWang CNorick BEl-Kishky ALiu JRen XSun YDyreson CLi FÖzsu M(2014)NewsNetExplorerProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2594537(1091-1094)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2594537

View Options

Login options

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

Cited By

Index Terms

Recommendations

Sparse representation for heterogeneous information networks

Mining Query-Based Subnetwork Outliers in Heterogeneous Information Networks

Iceberg Cube Query on Heterogeneous Information Networks