Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Social Network Analysis and Mining pp 707–715Cite as

Economic Network Analysis Based on Infection Models

  • Reference work entry
  • First Online:

Synonyms

Data mining and knowledge discovery in economic networks; Graphs in economy; Prediction of credit default and churn

Glossary

(Strong) Component:

maximal set of vertices that are reachable from each other by (directed) paths

Cluster:

a class of a partition of vertices.

Community:

a dense part of a graph (possibly overlapping)

Host graph:

the vertices are companies; the edges represent either money transfer or other types of connections

Intersection graph:

the vertices correspond to sets, and edges are drawn if the sets intersect

Transaction graph:

edges represent some transaction (call, money flow, etc.) among vertices (clients)

Definition

Infection Models

There are several types of models depending on the specialty of the field; for a brief introduction, see Chapter 7 of Jackson (2010). The type of events (default, churn, fraud, route of development) does not involve recovery or resistance, so simple percolation models suffice.

SI Models

For all the applications listed in this...

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   2,500.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   549.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  • Adamcsek B et al (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22:1021–1023

    Article  Google Scholar 

  • Asur S et al (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans Knowl Discov Data 3(4):16–36

    Article  Google Scholar 

  • Bascompte J et al (2007) Plant-animal mutualistic networks: the architecture of biodiversity. Annu Rev Ecol Evol Syst 38:567–593

    Article  MATH  Google Scholar 

  • Bass F (1969) A new product growth model for consumer durables. Manag Sci 15:215–227

    Article  MATH  Google Scholar 

  • Blondel VD et al (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 10:P10008

    Article  Google Scholar 

  • Blume L (1993) The statistical mechanics of strategic interaction. Games Econ Behav 5:387–424

    Article  MathSciNet  MATH  Google Scholar 

  • Bóta A et al (2011a) Dynamic communities and their detection. Acta Cybernetica 20:35–52

    Article  MATH  Google Scholar 

  • Bóta A et al (2011b) Systematic learning of edge probabilities in the Domingos-Richardson model. Int J Complex Syst Sci 1(2):115–118

    Google Scholar 

  • Bóta A et al (2012) Models for fully mapping the economic ties in Hungary before and during the recent crisis. In: Proceedings of crisis aftermath: economic policy changes in the EU and its member states, 8–9 Mar 2012

    Google Scholar 

  • Bóta A et al (2013) Approximations of the generalized cascade model. Acta Cybernetica 21:37–51

    Article  MathSciNet  MATH  Google Scholar 

  • Bóta A et al (2014) The inverse infection problem. In: Proceedings of the 2014 federated conference on computer science and information systems, IEEE Computer Society, pp 75–84.

    Google Scholar 

  • Bóta A et al (2015) Application of the inverse infection problem on bank transaction networks. CEJOR 23:345–356

    Article  MathSciNet  MATH  Google Scholar 

  • Bóta A et al (2016) Estimation of edge infection probabilities in the inverse infection problem. In: Fidanova S (ed) Recent advances in computational optimization. Studies on computational intelligence, vol 610. Springer, Heidelberg, pp 17–36

    Chapter  Google Scholar 

  • Bródka P et al (2013) GED: the method for group evolution discovery in social networks. Soc Netw Anal Min 3(1):1–14

    Article  Google Scholar 

  • Chen W et al (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Washington, pp 1029–1038

    Google Scholar 

  • Coleman J et al (1966) Medical innovations: a diffusion study. Bobbs Merrill, Indianapolis

    Google Scholar 

  • Csernenszky A et al (2009) The use of infections models in accounting and crediting. In: Proceedings of the challenges for analysis of the economy, the business, and social progress, international scientific conference, Szeged, pp 617–623

    Google Scholar 

  • Csizmadia L et al (2010) Community detection and its use in real graphs. In: Proceedings of the 13th international multiconference INFORMATION SOCIETY_IS 2010, pp 393–396

    Google Scholar 

  • Domingos P, Richardson M (2001) Mining the network value of costumers. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 57–66

    Chapter  Google Scholar 

  • Ellison G (1993) Learning, local interaction, and coordination. Econometrica 61(5):1047–1071

    Article  MathSciNet  MATH  Google Scholar 

  • Granovetter M (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380

    Article  Google Scholar 

  • Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443

    Article  Google Scholar 

  • Griechisch E, Pluhár A (2011) Community detection by using the extended modularity. Acta Cybernetica 20:69–85

    Article  MATH  Google Scholar 

  • Győrffy et al (2015) Direct marketing optimization using client relational graphs. Studia Univ Babes-Bolyai Informatica 59:137–149

    Google Scholar 

  • Haberly D, Wójcik D (2014) Regional blocks and imperial legacies: mapping the global offshore FDI network. Econ Geogr 91:251–280

    Article  Google Scholar 

  • Hidalgo CA et al (2007) The product space conditions the development of nations. Science 317:482–487

    Article  Google Scholar 

  • Horvát E-Á et al (2012) One plus one makes three (for social networks). PLoS One 7(4):e34740. https://doi.org/10.1371/journal.pone.0034740

    Article  Google Scholar 

  • Jackson MO (2010) Social and economic networks. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Kempe D et al (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 137–146

    Google Scholar 

  • Kivela M et al (2014) Multilayer networks. J Complex Networks 2(3):203–271

    Article  Google Scholar 

  • Merza A et al (2016) On the possible use of network science in the analysis of world trade. (in Hungarian) Közgazdasági Szemle (Econ Rev) LXIII(1):79–98

    Google Scholar 

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Palla G et al (2009) Social group dynamics in networks. In: Adaptive networks. Springer, Berlin/Heidelberg, pp 11–38

    Chapter  Google Scholar 

  • Srivastava A et al (2015) The unified model of social influence and its application in influence maximization. Soc Netw Anal Min 5(1):66:1–66:15

    Article  Google Scholar 

  • Tong G et al (2017) Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans Networking 25(1):112–125

    Article  Google Scholar 

  • Uzzi B (1996) The sources and consequences of embeddedness for the economic performance of organizations: the network effect. Am Sociol Rev 61(4):674–698

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by National Research, Development and Innovation Office – NKFIH Fund No. SNN-117879.

Author information

Authors and Affiliations

  1. Institute of Applied Sciences, University of Szeged, Szeged, Hungary

    M. Krész

  2. Institute of Informatics, University of Szeged, Szeged, Hungary

    A. Pluhár

Authors
  1. M. Krész
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Pluhár
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Krész .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Section Editor information

  1. Yahoo Inc, London, UK

    Fabrizio Silvestri

  2. University of Calabria, Via Pietro Bucci, 42C, 87036, Arcavacata di Rende, Italy

    Andrea Tagarelli

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Krész, M., Pluhár, A. (2018). Economic Network Analysis Based on Infection Models. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_29

Download citation

Publish with us

Policies and ethics

Search

Navigation