Skip to main content
SpringerLink
Log in
Book cover

International Conference on Conceptual Modeling

ER 2014: Conceptual Modeling pp 158–171Cite as

Network Analytics ER Model – Towards a Conceptual View of Network Analytics

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8824))

Abstract

This paper proposes a conceptual modelling paradigm for network analysis applications, called the Network Analytics ER model (NAER). Not only data requirements but also query requirements are captured by the conceptual description of network analysis applications. This unified analytical framework allows us to flexibly build a number of topology schemas on the basis of the underlying core schema, together with a collection of query topics that describe topological results of interest. In doing so, we can alleviate many issues in network analysis, such as performance, semantic integrity and dynamics of analysis.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bao, Z., Tay, Y.C., Zhou, J.: sonSchema: A conceptual schema for social networks. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) ER 2013. LNCS, vol. 8217, pp. 197–211. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  2. Brandes, U., Erlebach, T.: Network Analysis. LNCS, vol. 3418. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  3. Brughmans, T.: Connecting the dots: towards archaeological network analysis. Oxford Journal of Archaeology 29(3), 277–303 (2010)

    Article  Google Scholar 

  4. Chen, C., Song, I.-Y., Zhu, W.: Trends in conceptual modeling: Citation analysis of the ER Conference papers (1979-2005)

    Google Scholar 

  5. Chen, P.: The entity-relationship model – toward a unified view of data. ACM TODS 1(1), 9–36 (1976)

    Article  Google Scholar 

  6. Cohen, S., Ebel, L., Kimelfeld, B.: A social network database that learns how to answer queries. In: CIDR (2013)

    Google Scholar 

  7. Dries, A., Nijssen, S., De Raedt, L.: A query language for analyzing networks. In: CIKM, pp. 485–494 (2009)

    Google Scholar 

  8. Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  9. Freeman, L.C.: Centrality in social networks conceptual clarification. Social Networks 1(3), 215–239 (1979)

    Article  Google Scholar 

  10. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Huisman, M., Van Duijn, M.A.: Software for social network analysis. Models and Methods in Social Network Analysis, 270–316 (2005)

    Google Scholar 

  12. Jamakovic, A., Uhlig, S.: On the relationships between topological measures in real-world networks. Networks and Heterogeneous Media 3(2), 345 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Link Mining: Models, Algorithms, and Applications, pp. 337–357. Springer (2010)

    Google Scholar 

  14. Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 1150–1170 (2011)

    Article  Google Scholar 

  15. Newman, M.E.: Scientific collaboration networks. i. network construction and fundamental results. Physical Review E 64(1), 016131 (2001)

    Google Scholar 

  16. Pavlopoulos, G.A., Wegener, A.-L., Schneider, R.: A survey of visualization tools for biological network analysis. Biodata Mining 1(1), 1–11 (2008)

    Article  Google Scholar 

  17. Sakr, S., Elnikety, S., He, Y.: G-SPARQL: A hybrid engine for querying large attributed graphs. In: CIKM, pp. 335–344 (2012)

    Google Scholar 

  18. Sheng, L., Ozsoyoglu, Z.M., Ozsoyoglu, G.: A graph query language and its query processing. In: ICDE, pp. 572–581 (1999)

    Google Scholar 

  19. Thalheim, B.: Entity-relationship modeling: foundations of database technology. Springer (2000)

    Google Scholar 

  20. Wicker, J., Richter, L., Kessler, K., Kramer, S.: SINDBAD and SiQL: An inductive database and query language in the relational model. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 690–694. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  21. Wood, P.: Query languages for graph databases. ACM SIGMOD Record 41(1), 50–60 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research School of Computer Science, The Australian National University, Australia

    Qing Wang

Authors
  1. Qing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Information, University of Toronto, 140 St. George St., M5S 3G6, Toronto, ON, Canada

    Eric Yu

  2. Dept of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Gillian Dobbie

  3. Lehrstuhl Informatik 5, RWTH Aachen University, Ahornstr. 55, 52056, Aachen, Germany

    Matthias Jarke

  4. Penn State University, 316B Information Sciences and Technology Building, 16802, University Park, PA, USA

    Sandeep Purao

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, Q. (2014). Network Analytics ER Model – Towards a Conceptual View of Network Analytics. In: Yu, E., Dobbie, G., Jarke, M., Purao, S. (eds) Conceptual Modeling. ER 2014. Lecture Notes in Computer Science, vol 8824. Springer, Cham. https://doi.org/10.1007/978-3-319-12206-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12206-9_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12205-2

  • Online ISBN: 978-3-319-12206-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation