TY - GEN
T1 - Networkalization of network–unlike entities
T2 - 3rd Conference on Creativity in Intelligent Technologies and Data Science, CIT and DS 2019
AU - Berestneva, Olga
AU - Marukhina, Olga
AU - Rossodivita, Alessandra
AU - Tikhomirov, Alexei
AU - Trufanov, Andrey
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Complex networks
KW - Converting
KW - Network-like objects
KW - Network–Unlike Entities
KW - Scaling
KW - Spatial and Temporal Data
UR - http://www.scopus.com/inward/record.url?scp=85071579091&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071579091&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29743-5_11
DO - 10.1007/978-3-030-29743-5_11
M3 - Conference contribution
AN - SCOPUS:85071579091
SN - 9783030297428
T3 - Communications in Computer and Information Science
SP - 143
EP - 151
BT - Creativity in Intelligent Technologies and Data Science - 3rd Conference, CIT and DS 2019, Proceedings
A2 - Kravets, Alla G.
A2 - Groumpos, Peter P.
A2 - Shcherbakov, Maxim
A2 - Kultsova, Marina
PB - Springer Verlag
Y2 - 16 September 2019 through 19 September 2019
ER -