skip to main content
10.1145/2939672.2939815acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Meta Structure: Computing Relevance in Large Heterogeneous Information Networks

Published: 13 August 2016 Publication History

Abstract

A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures can be used in various applications, including entity resolution, recommendation, and information retrieval. Several studies have investigated the use of HIN information for relevance computation, however, most of them only utilize simple structure, such as path, to measure the similarity between objects. In this paper, we propose to use meta structure, which is a directed acyclic graph of object types with edge types connecting in between, to measure the proximity between objects. The strength of meta structure is that it can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we further design an algorithm with data structures proposed to support their evaluation. Our extensive experiments on YAGO and DBLP show that meta structure-based relevance is more effective than state-of-the-art approaches, and can be efficiently computed.

References

[1]
S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives. Dbpedia: a nucleus for a web of open data. In ISWC, pages 722--735. Springer-Verlag, 2007.
[2]
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD, pages 1247--1250, 2008.
[3]
S. Chakrabarti. Dynamic personalized pagerank in entity-relation graphs. In WWW, pages 571--580, 2007.
[4]
J. Chen, W. Dai, Y. Sun, and J. Dy. Clustering and ranking in heterogeneous information networks via gamma-poisson model. NTm, 1000:1.
[5]
N. Jayaram, M. Gupta, A. Khan, C. Li, X. Yan, and R. Elmasri. Gqbe: Querying knowledge graphs by example entity tuples. In ICDE, pages 1250--1253. IEEE, 2014.
[6]
G. Jeh and J. Widom. SimRank: a measure of structural-context similarity. In KDD, pages 538--543, 2002.
[7]
N. Lao and W. W. Cohen. Relational retrieval using a combination of path-constrained random walks. Machine learning, 81(1):53--67, 2010.
[8]
M. Ley. Dblp computer science bibliography. 2005.
[9]
D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol., 58(7), 2007.
[10]
X. Liu, Y. Yu, C. Guo, and Y. Sun. Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation. In CIKM, pages 121--130, 2014.
[11]
X. Liu, Y. Yu, C. Guo, Y. Sun, and L. Gao. Full-text based context-rich heterogeneous network mining approach for citation recommendation. In JCDL, pages 361--370, 2014.
[12]
C. Meng, R. Cheng, S. Maniu, P. Senellart, and W. Zhang. Discovering meta-paths in large heterogeneous information networks. In WWW, pages 754--764, 2015.
[13]
D. Mottin, M. Lissandrini, Y. Velegrakis, and T. Palpanas. Exemplar queries: Give me an example of what you need. PVLDB, 7(5):365--376, 2014.
[14]
E. Prud-Hommeaux, A. Seaborne, et al. Sparql query language for rdf. W3C recommendation, 15, 2008.
[15]
F. M. Suchanek, G. Kasneci, and G. Weikum. Yago: a core of semantic knowledge. In WWW, pages 697--706, 2007.
[16]
Y. Sun, R. Barber, M. Gupta, C. C. Aggarwal, and J. Han. Co-author relationship prediction in heterogeneous bibliographic networks. In ASONAM, pages 121--128, 2011.
[17]
Y. Sun, J. Han, C. C. Aggarwal, and N. V. Chawla. When will it happen?: relationship prediction in heterogeneous information networks. In WSDM, pages 663--672, 2012.
[18]
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 PVLDB, pages 992--1003, 2011.
[19]
Y. Sun, B. Norick, J. Han, X. Yan, P. S. Yu, and X. Yu. Pathselclus: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. TKDD, 7(3):11, 2013.
[20]
Y. Yang, N. Chawla, Y. Sun, and J. Hani. Predicting links in multi-relational and heterogeneous networks. In ICDM, pages 755--764, 2012.
[21]
X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han. Personalized entity recommendation: A heterogeneous information network approach. In WSDM, pages 283--292, 2014.
[22]
X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal, Q. Gu, B. Norick, and J. Han. Recommendation in heterogeneous information networks with implicit user feedback. In RecSys, pages 347--350, 2013.

Cited By

View all
  • (2025)Heterogeneous Social Event Detection via Hyperbolic Graph RepresentationsIEEE Transactions on Big Data10.1109/TBDATA.2024.338101711:1(115-129)Online publication date: Feb-2025
  • (2025)A textual data-driven method to measure the capabilities and core paths of different digital technologies to improve supply chain resilienceInternational Journal of Logistics Research and Applications10.1080/13675567.2024.2446247(1-34)Online publication date: 2-Jan-2025
  • (2024)StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information NetworksApplied Sciences10.3390/app1402093514:2(935)Online publication date: 22-Jan-2024
  • Show More Cited By

Index Terms

  1. Meta Structure: Computing Relevance in Large Heterogeneous Information Networks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2016
    2176 pages
    ISBN:9781450342322
    DOI:10.1145/2939672
    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: 13 August 2016

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. heterogeneous information network
    2. meta path
    3. meta structure
    4. relevance

    Qualifiers

    • Research-article

    Funding Sources

    • Research Grant Council of Hong Kong (RGC) GRF
    • NSF CAREER

    Conference

    KDD '16
    Sponsor:

    Acceptance Rates

    KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

    Upcoming Conference

    KDD '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)104
    • Downloads (Last 6 weeks)10
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)Heterogeneous Social Event Detection via Hyperbolic Graph RepresentationsIEEE Transactions on Big Data10.1109/TBDATA.2024.338101711:1(115-129)Online publication date: Feb-2025
    • (2025)A textual data-driven method to measure the capabilities and core paths of different digital technologies to improve supply chain resilienceInternational Journal of Logistics Research and Applications10.1080/13675567.2024.2446247(1-34)Online publication date: 2-Jan-2025
    • (2024)StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information NetworksApplied Sciences10.3390/app1402093514:2(935)Online publication date: 22-Jan-2024
    • (2024)A review on network representation learning with multi-granularity perspectiveIntelligent Data Analysis10.3233/IDA-22732828:1(3-32)Online publication date: 3-Feb-2024
    • (2024)BSIN: A Behavior Schema of Information Networks Based on Approximate BisimulationTsinghua Science and Technology10.26599/TST.2023.901008129:4(1092-1104)Online publication date: Aug-2024
    • (2024)A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge GraphInternational Journal of Intelligent Systems10.1155/2024/41694022024Online publication date: 1-Jan-2024
    • (2024)Multi-Behavior Recommendation with Personalized Directed Acyclic Behavior GraphsACM Transactions on Information Systems10.1145/369641743:1(1-30)Online publication date: 19-Sep-2024
    • (2024)Large Language Model-driven Meta-structure Discovery in Heterogeneous Information NetworkProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671965(307-318)Online publication date: 25-Aug-2024
    • (2024)Meta Structure Search for Link Weight Prediction in Heterogeneous GraphsICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448349(5195-5199)Online publication date: 14-Apr-2024
    • (2024)Learning Resource Recommendation Model Based on Collaborative Knowledge Graph Attention NetworksIEEE Access10.1109/ACCESS.2024.347774012(153232-153242)Online publication date: 2024
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media