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Synthetic generation of spatial graphs
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-0368-8037
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. (Ecological Modeling, Ekologisk modellering)ORCID-id: 0000-0002-3965-7371
Universitat Autònoma de Barcelona, Spain / Center for Cybersecurity Research of Catalonia (CYBERCAT), Spain.
Center for Cybersecurity Research of Catalonia (CYBERCAT), Spain / Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
2018 (engelsk)Inngår i: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 32, nr 12, s. 2364-2378Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Graphs can be used to model many different types of interaction networks, for example, online social networks or animal transport networks. Several algorithms have thus been introduced to build graphs according to some predefined conditions. In this paper, we present an algorithm that generates spatial graphs with a given degree sequence. In spatial graphs, nodes are located in a space equiped with a metric. Our goal is to define a graph in such a way that the nodes and edges are positioned according to an underlying metric. More particularly, we have constructed a greedy algorithm that generates nodes proportional to an underlying probability distribution from the spatial structure, and then generates edges inversely proportional to the Euclidean distance between nodes. The algorithm first generates a graph that can be a multigraph, and then corrects multiedges. Our motivation is in data privacy for social networks, where a key problem is the ability to build synthetic graphs. These graphs need to satisfy a set of required properties (e.g., the degrees of the nodes) but also be realistic, and thus, nodes (individuals) should be located according to a spatial structure and connections should be added taking into account nearness.

sted, utgiver, år, opplag, sider
John Wiley & Sons, 2018. Vol. 32, nr 12, s. 2364-2378
Emneord [en]
data privacy, graphs generating algorithms, network modeling, spatial graphs
HSV kategori
Forskningsprogram
Ekologisk modellering; Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifikatorer
URN: urn:nbn:se:his:diva-16290DOI: 10.1002/int.22034ISI: 000448278500004Scopus ID: 2-s2.0-85054373026OAI: oai:DiVA.org:his-16290DiVA, id: diva2:1254993
Tilgjengelig fra: 2018-10-11 Laget: 2018-10-11 Sist oppdatert: 2019-02-14bibliografisk kontrollert

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