Economic Network Analysis Based on Infection Models
- (Strong) Component
maximal set of vertices that are reachable from each other by (directed) paths
a class of a partition of vertices.
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)
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.
For all the applications listed in this...
This research was partially supported by National Research, Development and Innovation Office – NKFIH Fund No. SNN-117879.
- Bóta A et al (2011b) Systematic learning of edge probabilities in the Domingos-Richardson model. Int J Complex Syst Sci 1(2):115–118Google 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 2012Google 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
- 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 1029–1038Google Scholar
- Coleman J et al (1966) Medical innovations: a diffusion study. Bobbs Merrill, IndianapolisGoogle 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 617–623Google 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 393–396Google Scholar
- Győrffy et al (2015) Direct marketing optimization using client relational graphs. Studia Univ Babes-Bolyai Informatica 59:137–149Google 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 137–146Google 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):79–98Google Scholar