Networkalization of network–unlike entities: How to preserve encoded information

Olga Berestneva, Olga Marukhina, Alessandra Rossodivita, Alexei Tikhomirov, Andrey Trufanov

Research output: Chapter in Book/Report/Conference proceedingConference contribution


More than for twenty years network science with complex networks as its basic component has brought the idea to analyze a wide spectrum of entities through a focus on relations between the actors and has implemented the concomitant powerful instruments of the analysis. Some entities (objects, processes, and data) with their intrinsic web nature might be interpreted as networks naturally. Network ontology of another family, Network–Unlike Entities, e.g. spatial and temporal ones, is severely ambiguous and encounters with tough problems on the way to convert data into networks. We concentrate on separation the properties of data in line with their scale diversity – in the distance, time, and nature and suggested a 3 step algorithm (scale-based technique) to convert Network–Unlike Entities into complex networks. The technique was applied for networkalization of landscape and land use maps representing Olkhon district, Irkutsk region, Baikal Lake territory, RF. It was found that the technique with its coarse-graining and area-like connecting conserves natural information inherent to the entities and imbeds accordingly scale-free and small world properties into output networks, thus making them really complex in their structure.

Original languageEnglish
Title of host publicationCreativity in Intelligent Technologies and Data Science - 3rd Conference, CIT and DS 2019, Proceedings
EditorsAlla G. Kravets, Peter P. Groumpos, Maxim Shcherbakov, Marina Kultsova
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783030297428
Publication statusPublished - 2019
Event3rd Conference on Creativity in Intelligent Technologies and Data Science, CIT and DS 2019 - Volgograd, Russian Federation
Duration: 16 Sep 201919 Sep 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference3rd Conference on Creativity in Intelligent Technologies and Data Science, CIT and DS 2019
CountryRussian Federation


  • Complex networks
  • Converting
  • Network-like objects
  • Network–Unlike Entities
  • Scaling
  • Spatial and Temporal Data

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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