Core-based Approach to Measure Pairwise Layer Similarity in Multiplex Network Article Swipe
Debasis Mohapatra
,
Sourav Kumar Bhoi
,
Kalyan Kumar Jena
,
Chittaranjan Mallick
,
Kshira Sagar Sahoo
,
N. Z. Jhanjhi
,
Mehedi Masud
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2022.024561
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2022.024561
Most of the recent works on network science are focused on investigating various interactions among a set of entities present in a system that can be represented by multiplex network. Each type of relationship is treated as a layer of multiplex network. Some of the recent works on multiplex networks are focused on deriving layer similarity from node similarity where node similarity is evaluated using neighborhood similarity measures like cosine similarity and Jaccard similarity. But this type of analysis lacks in finding the set of nodes having the same influence in both the network. The discovery of influence similarity between the layers of multiplex networks helps in strategizing cascade effect, influence maximization, network controllability, etc. Towards this end, this paper proposes a pairwise similarity evaluation of layers based on a set of common core nodes of the layers. It considers the number of nodes present in the common core set, the average clustering coefficient of the common core set, and fractional influence capacity of the common core set to quantify layer similarity. The experiment is carried out on three real multiplex networks. As the proposed notion of similarity uses a different aspect of layer similarity than the existing one, a low positive correlation (close to non-correlation) is found between the proposed and existing approach of layer similarity. The result demonstrates that the degree of coreness difference is less for the datasets in the proposed method than the existing one. The existing method reports the coreness difference to be 40% and 18.4% for the datasets CS-AARHUS and EU-AIR TRANSPORTATION MULTIPLEX respectively whereas it is found to be 20% and 8.1% using proposed approach.
Related Topics
Concepts
Jaccard index
Similarity (geometry)
Computer science
Data mining
Pairwise comparison
Node (physics)
Set (abstract data type)
Core (optical fiber)
Layer (electronics)
Cosine similarity
Cluster analysis
Similarity measure
Network layer
Artificial intelligence
Programming language
Engineering
Organic chemistry
Chemistry
Structural engineering
Telecommunications
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/iasc.2022.024561
- https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225729937
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4225729937Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/iasc.2022.024561Digital Object Identifier
- Title
-
Core-based Approach to Measure Pairwise Layer Similarity in Multiplex NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Debasis Mohapatra, Sourav Kumar Bhoi, Kalyan Kumar Jena, Chittaranjan Mallick, Kshira Sagar Sahoo, N. Z. Jhanjhi, Mehedi MasudList of authors in order
- Landing page
-
https://doi.org/10.32604/iasc.2022.024561Publisher landing page
- PDF URL
-
https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdfDirect OA link when available
- Concepts
-
Jaccard index, Similarity (geometry), Computer science, Data mining, Pairwise comparison, Node (physics), Set (abstract data type), Core (optical fiber), Layer (electronics), Cosine similarity, Cluster analysis, Similarity measure, Network layer, Artificial intelligence, Programming language, Engineering, Organic chemistry, Chemistry, Structural engineering, Telecommunications, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4225729937 |
|---|---|
| doi | https://doi.org/10.32604/iasc.2022.024561 |
| ids.doi | https://doi.org/10.32604/iasc.2022.024561 |
| ids.openalex | https://openalex.org/W4225729937 |
| fwci | 1.53843396 |
| type | article |
| title | Core-based Approach to Measure Pairwise Layer Similarity in Multiplex Network |
| biblio.issue | 1 |
| biblio.volume | 34 |
| biblio.last_page | 64 |
| biblio.first_page | 51 |
| topics[0].id | https://openalex.org/T10064 |
| topics[0].field.id | https://openalex.org/fields/31 |
| topics[0].field.display_name | Physics and Astronomy |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3109 |
| topics[0].subfield.display_name | Statistical and Nonlinear Physics |
| topics[0].display_name | Complex Network Analysis Techniques |
| topics[1].id | https://openalex.org/T12592 |
| topics[1].field.id | https://openalex.org/fields/31 |
| topics[1].field.display_name | Physics and Astronomy |
| topics[1].score | 0.9944999814033508 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3109 |
| topics[1].subfield.display_name | Statistical and Nonlinear Physics |
| topics[1].display_name | Opinion Dynamics and Social Influence |
| topics[2].id | https://openalex.org/T10887 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9890000224113464 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Bioinformatics and Genomic Networks |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C203519979 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8741105794906616 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q865360 |
| concepts[0].display_name | Jaccard index |
| concepts[1].id | https://openalex.org/C103278499 |
| concepts[1].level | 3 |
| concepts[1].score | 0.8102218508720398 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q254465 |
| concepts[1].display_name | Similarity (geometry) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7045914530754089 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C124101348 |
| concepts[3].level | 1 |
| concepts[3].score | 0.616676390171051 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[3].display_name | Data mining |
| concepts[4].id | https://openalex.org/C184898388 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5841017961502075 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1435712 |
| concepts[4].display_name | Pairwise comparison |
| concepts[5].id | https://openalex.org/C62611344 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5805187821388245 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1062658 |
| concepts[5].display_name | Node (physics) |
| concepts[6].id | https://openalex.org/C177264268 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5790570974349976 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[6].display_name | Set (abstract data type) |
| concepts[7].id | https://openalex.org/C2164484 |
| concepts[7].level | 2 |
| concepts[7].score | 0.527778148651123 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q5170150 |
| concepts[7].display_name | Core (optical fiber) |
| concepts[8].id | https://openalex.org/C2779227376 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5068731904029846 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q6505497 |
| concepts[8].display_name | Layer (electronics) |
| concepts[9].id | https://openalex.org/C2780762811 |
| concepts[9].level | 3 |
| concepts[9].score | 0.4476623833179474 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1784941 |
| concepts[9].display_name | Cosine similarity |
| concepts[10].id | https://openalex.org/C73555534 |
| concepts[10].level | 2 |
| concepts[10].score | 0.44313865900039673 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[10].display_name | Cluster analysis |
| concepts[11].id | https://openalex.org/C2776517306 |
| concepts[11].level | 2 |
| concepts[11].score | 0.43243318796157837 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q29017317 |
| concepts[11].display_name | Similarity measure |
| concepts[12].id | https://openalex.org/C117468950 |
| concepts[12].level | 3 |
| concepts[12].score | 0.4179900586605072 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q208074 |
| concepts[12].display_name | Network layer |
| concepts[13].id | https://openalex.org/C154945302 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3907550573348999 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[13].display_name | Artificial intelligence |
| concepts[14].id | https://openalex.org/C199360897 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[14].display_name | Programming language |
| concepts[15].id | https://openalex.org/C127413603 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[15].display_name | Engineering |
| concepts[16].id | https://openalex.org/C178790620 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11351 |
| concepts[16].display_name | Organic chemistry |
| concepts[17].id | https://openalex.org/C185592680 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[17].display_name | Chemistry |
| concepts[18].id | https://openalex.org/C66938386 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[18].display_name | Structural engineering |
| concepts[19].id | https://openalex.org/C76155785 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[19].display_name | Telecommunications |
| concepts[20].id | https://openalex.org/C115961682 |
| concepts[20].level | 2 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[20].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/jaccard-index |
| keywords[0].score | 0.8741105794906616 |
| keywords[0].display_name | Jaccard index |
| keywords[1].id | https://openalex.org/keywords/similarity |
| keywords[1].score | 0.8102218508720398 |
| keywords[1].display_name | Similarity (geometry) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7045914530754089 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/data-mining |
| keywords[3].score | 0.616676390171051 |
| keywords[3].display_name | Data mining |
| keywords[4].id | https://openalex.org/keywords/pairwise-comparison |
| keywords[4].score | 0.5841017961502075 |
| keywords[4].display_name | Pairwise comparison |
| keywords[5].id | https://openalex.org/keywords/node |
| keywords[5].score | 0.5805187821388245 |
| keywords[5].display_name | Node (physics) |
| keywords[6].id | https://openalex.org/keywords/set |
| keywords[6].score | 0.5790570974349976 |
| keywords[6].display_name | Set (abstract data type) |
| keywords[7].id | https://openalex.org/keywords/core |
| keywords[7].score | 0.527778148651123 |
| keywords[7].display_name | Core (optical fiber) |
| keywords[8].id | https://openalex.org/keywords/layer |
| keywords[8].score | 0.5068731904029846 |
| keywords[8].display_name | Layer (electronics) |
| keywords[9].id | https://openalex.org/keywords/cosine-similarity |
| keywords[9].score | 0.4476623833179474 |
| keywords[9].display_name | Cosine similarity |
| keywords[10].id | https://openalex.org/keywords/cluster-analysis |
| keywords[10].score | 0.44313865900039673 |
| keywords[10].display_name | Cluster analysis |
| keywords[11].id | https://openalex.org/keywords/similarity-measure |
| keywords[11].score | 0.43243318796157837 |
| keywords[11].display_name | Similarity measure |
| keywords[12].id | https://openalex.org/keywords/network-layer |
| keywords[12].score | 0.4179900586605072 |
| keywords[12].display_name | Network layer |
| keywords[13].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[13].score | 0.3907550573348999 |
| keywords[13].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.32604/iasc.2022.024561 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S40639465 |
| locations[0].source.issn | 1079-8587, 2326-005X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1079-8587 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Intelligent Automation & Soft Computing |
| locations[0].source.host_organization | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_name | Taylor & Francis |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_lineage_names | Taylor & Francis |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Intelligent Automation & Soft Computing |
| locations[0].landing_page_url | https://doi.org/10.32604/iasc.2022.024561 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5020439618 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4302-1974 |
| authorships[0].author.display_name | Debasis Mohapatra |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2799452066 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of CSE, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[0].institutions[0].id | https://openalex.org/I2799452066 |
| authorships[0].institutions[0].ror | https://ror.org/034pbde03 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I2799452066, https://openalex.org/I33585257 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Maharaja Engineering College |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Debasis Mohapatra |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of CSE, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[1].author.id | https://openalex.org/A5044453219 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5173-3453 |
| authorships[1].author.display_name | Sourav Kumar Bhoi |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I2799452066 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of CSE, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[1].institutions[0].id | https://openalex.org/I2799452066 |
| authorships[1].institutions[0].ror | https://ror.org/034pbde03 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I2799452066, https://openalex.org/I33585257 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Maharaja Engineering College |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sourav Kumar Bhoi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of CSE, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[2].author.id | https://openalex.org/A5072390737 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2476-5644 |
| authorships[2].author.display_name | Kalyan Kumar Jena |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kalyan Kumar Jena |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5112465833 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Chittaranjan Mallick |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I2799452066 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of CSE, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I2799452066 |
| authorships[3].affiliations[1].raw_affiliation_string | Department of Basic Science, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[3].institutions[0].id | https://openalex.org/I2799452066 |
| authorships[3].institutions[0].ror | https://ror.org/034pbde03 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I2799452066, https://openalex.org/I33585257 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | Maharaja Engineering College |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chittaranjan Mallick |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Basic Science, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India, Department of CSE, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India |
| authorships[4].author.id | https://openalex.org/A5049724493 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6435-5738 |
| authorships[4].author.display_name | Kshira Sagar Sahoo |
| authorships[4].countries | IN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210131147 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, SRM University, Amaravati, AP, 522240, India |
| authorships[4].institutions[0].id | https://openalex.org/I4210131147 |
| authorships[4].institutions[0].ror | https://ror.org/037skf023 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I145286018, https://openalex.org/I4210131147 |
| authorships[4].institutions[0].country_code | IN |
| authorships[4].institutions[0].display_name | SRM University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Kshira Sagar Sahoo |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer Science and Engineering, SRM University, Amaravati, AP, 522240, India |
| authorships[5].author.id | https://openalex.org/A5029582839 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8116-4733 |
| authorships[5].author.display_name | N. Z. Jhanjhi |
| authorships[5].countries | MY |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210143550 |
| authorships[5].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya, 47500, Selangor, Malaysia |
| authorships[5].institutions[0].id | https://openalex.org/I4210143550 |
| authorships[5].institutions[0].ror | https://ror.org/0498pcx51 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210143550 |
| authorships[5].institutions[0].country_code | MY |
| authorships[5].institutions[0].display_name | Taylor's University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | N. Z. Jhanjhi |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya, 47500, Selangor, Malaysia |
| authorships[6].author.id | https://openalex.org/A5045359397 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-6019-7245 |
| authorships[6].author.display_name | Mehedi Masud |
| authorships[6].countries | SA |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I179331831 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia |
| authorships[6].institutions[0].id | https://openalex.org/I179331831 |
| authorships[6].institutions[0].ror | https://ror.org/014g1a453 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I179331831 |
| authorships[6].institutions[0].country_code | SA |
| authorships[6].institutions[0].display_name | Taif University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Mehedi Masud |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Core-based Approach to Measure Pairwise Layer Similarity in Multiplex Network |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10064 |
| primary_topic.field.id | https://openalex.org/fields/31 |
| primary_topic.field.display_name | Physics and Astronomy |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3109 |
| primary_topic.subfield.display_name | Statistical and Nonlinear Physics |
| primary_topic.display_name | Complex Network Analysis Techniques |
| related_works | https://openalex.org/W4381948805, https://openalex.org/W4214483597, https://openalex.org/W4220978606, https://openalex.org/W4220894110, https://openalex.org/W4388145912, https://openalex.org/W4286850906, https://openalex.org/W4313532769, https://openalex.org/W1437580529, https://openalex.org/W2307895033, https://openalex.org/W3048951355 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.32604/iasc.2022.024561 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S40639465 |
| best_oa_location.source.issn | 1079-8587, 2326-005X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1079-8587 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Intelligent Automation & Soft Computing |
| best_oa_location.source.host_organization | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_name | Taylor & Francis |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_lineage_names | Taylor & Francis |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Intelligent Automation & Soft Computing |
| best_oa_location.landing_page_url | https://doi.org/10.32604/iasc.2022.024561 |
| primary_location.id | doi:10.32604/iasc.2022.024561 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S40639465 |
| primary_location.source.issn | 1079-8587, 2326-005X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1079-8587 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Intelligent Automation & Soft Computing |
| primary_location.source.host_organization | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_name | Taylor & Francis |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_lineage_names | Taylor & Francis |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24561/TSP_IASC_24561.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Intelligent Automation & Soft Computing |
| primary_location.landing_page_url | https://doi.org/10.32604/iasc.2022.024561 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W6767419142, https://openalex.org/W2949367643, https://openalex.org/W2070897863, https://openalex.org/W2801338733, https://openalex.org/W2995611129, https://openalex.org/W2072688741, https://openalex.org/W2402962589, https://openalex.org/W3140370734, https://openalex.org/W3196732644, https://openalex.org/W3087866447, https://openalex.org/W6775566504, https://openalex.org/W2961514605, https://openalex.org/W2981737321, https://openalex.org/W1588378840, https://openalex.org/W2975867415, https://openalex.org/W2728528915, https://openalex.org/W2911805674, https://openalex.org/W2050766178, https://openalex.org/W2775142478, https://openalex.org/W2806997610, https://openalex.org/W3140497830, https://openalex.org/W3174528304, https://openalex.org/W3171898786, https://openalex.org/W3202336477, https://openalex.org/W2133131640, https://openalex.org/W2037967274, https://openalex.org/W6630156116, https://openalex.org/W2004210302, https://openalex.org/W2972575889, https://openalex.org/W2799796486, https://openalex.org/W3012466642, https://openalex.org/W3101456328, https://openalex.org/W3101642834, https://openalex.org/W3102247099 |
| referenced_works_count | 34 |
| abstract_inverted_index.a | 15, 21, 37, 121, 129, 189, 199 |
| abstract_inverted_index.As | 182 |
| abstract_inverted_index.It | 138 |
| abstract_inverted_index.as | 36 |
| abstract_inverted_index.be | 25, 247, 265 |
| abstract_inverted_index.by | 27 |
| abstract_inverted_index.in | 20, 80, 90, 106, 145, 231 |
| abstract_inverted_index.is | 34, 62, 174, 206, 226, 262 |
| abstract_inverted_index.it | 261 |
| abstract_inverted_index.of | 1, 17, 32, 39, 43, 77, 84, 96, 102, 125, 131, 135, 142, 154, 163, 186, 192, 214, 223 |
| abstract_inverted_index.on | 5, 10, 47, 52, 128, 177 |
| abstract_inverted_index.to | 168, 204, 246, 264 |
| abstract_inverted_index.20% | 266 |
| abstract_inverted_index.40% | 248 |
| abstract_inverted_index.But | 74 |
| abstract_inverted_index.The | 94, 172, 217, 239 |
| abstract_inverted_index.and | 71, 159, 211, 249, 255, 267 |
| abstract_inverted_index.are | 8, 50 |
| abstract_inverted_index.can | 24 |
| abstract_inverted_index.for | 228, 251 |
| abstract_inverted_index.low | 200 |
| abstract_inverted_index.out | 176 |
| abstract_inverted_index.set | 16, 83, 130, 167 |
| abstract_inverted_index.the | 2, 44, 82, 87, 92, 100, 136, 140, 146, 150, 155, 164, 183, 196, 209, 221, 229, 232, 236, 243, 252 |
| abstract_inverted_index.8.1% | 268 |
| abstract_inverted_index.Each | 30 |
| abstract_inverted_index.Most | 0 |
| abstract_inverted_index.Some | 42 |
| abstract_inverted_index.both | 91 |
| abstract_inverted_index.core | 133, 148, 157, 166 |
| abstract_inverted_index.end, | 117 |
| abstract_inverted_index.etc. | 114 |
| abstract_inverted_index.from | 56 |
| abstract_inverted_index.less | 227 |
| abstract_inverted_index.like | 68 |
| abstract_inverted_index.node | 57, 60 |
| abstract_inverted_index.one, | 198 |
| abstract_inverted_index.one. | 238 |
| abstract_inverted_index.real | 179 |
| abstract_inverted_index.same | 88 |
| abstract_inverted_index.set, | 149, 158 |
| abstract_inverted_index.than | 195, 235 |
| abstract_inverted_index.that | 23, 220 |
| abstract_inverted_index.this | 75, 116, 118 |
| abstract_inverted_index.type | 31, 76 |
| abstract_inverted_index.uses | 188 |
| abstract_inverted_index.18.4% | 250 |
| abstract_inverted_index.among | 14 |
| abstract_inverted_index.based | 127 |
| abstract_inverted_index.found | 207, 263 |
| abstract_inverted_index.helps | 105 |
| abstract_inverted_index.lacks | 79 |
| abstract_inverted_index.layer | 38, 54, 170, 193, 215 |
| abstract_inverted_index.nodes | 85, 134, 143 |
| abstract_inverted_index.paper | 119 |
| abstract_inverted_index.three | 178 |
| abstract_inverted_index.using | 64, 269 |
| abstract_inverted_index.where | 59 |
| abstract_inverted_index.works | 4, 46 |
| abstract_inverted_index.(close | 203 |
| abstract_inverted_index.EU-AIR | 256 |
| abstract_inverted_index.aspect | 191 |
| abstract_inverted_index.common | 132, 147, 156, 165 |
| abstract_inverted_index.cosine | 69 |
| abstract_inverted_index.degree | 222 |
| abstract_inverted_index.having | 86 |
| abstract_inverted_index.layers | 101, 126 |
| abstract_inverted_index.method | 234, 241 |
| abstract_inverted_index.notion | 185 |
| abstract_inverted_index.number | 141 |
| abstract_inverted_index.recent | 3, 45 |
| abstract_inverted_index.result | 218 |
| abstract_inverted_index.system | 22 |
| abstract_inverted_index.Jaccard | 72 |
| abstract_inverted_index.Towards | 115 |
| abstract_inverted_index.average | 151 |
| abstract_inverted_index.between | 99, 208 |
| abstract_inverted_index.carried | 175 |
| abstract_inverted_index.cascade | 108 |
| abstract_inverted_index.effect, | 109 |
| abstract_inverted_index.finding | 81 |
| abstract_inverted_index.focused | 9, 51 |
| abstract_inverted_index.layers. | 137 |
| abstract_inverted_index.network | 6, 112 |
| abstract_inverted_index.present | 19, 144 |
| abstract_inverted_index.reports | 242 |
| abstract_inverted_index.science | 7 |
| abstract_inverted_index.treated | 35 |
| abstract_inverted_index.various | 12 |
| abstract_inverted_index.whereas | 260 |
| abstract_inverted_index.analysis | 78 |
| abstract_inverted_index.approach | 213 |
| abstract_inverted_index.capacity | 162 |
| abstract_inverted_index.coreness | 224, 244 |
| abstract_inverted_index.datasets | 230, 253 |
| abstract_inverted_index.deriving | 53 |
| abstract_inverted_index.entities | 18 |
| abstract_inverted_index.existing | 197, 212, 237, 240 |
| abstract_inverted_index.measures | 67 |
| abstract_inverted_index.network. | 29, 41, 93 |
| abstract_inverted_index.networks | 49, 104 |
| abstract_inverted_index.pairwise | 122 |
| abstract_inverted_index.positive | 201 |
| abstract_inverted_index.proposed | 184, 210, 233, 270 |
| abstract_inverted_index.proposes | 120 |
| abstract_inverted_index.quantify | 169 |
| abstract_inverted_index.CS-AARHUS | 254 |
| abstract_inverted_index.MULTIPLEX | 258 |
| abstract_inverted_index.approach. | 271 |
| abstract_inverted_index.considers | 139 |
| abstract_inverted_index.different | 190 |
| abstract_inverted_index.discovery | 95 |
| abstract_inverted_index.evaluated | 63 |
| abstract_inverted_index.influence | 89, 97, 110, 161 |
| abstract_inverted_index.multiplex | 28, 40, 48, 103, 180 |
| abstract_inverted_index.networks. | 181 |
| abstract_inverted_index.clustering | 152 |
| abstract_inverted_index.difference | 225, 245 |
| abstract_inverted_index.evaluation | 124 |
| abstract_inverted_index.experiment | 173 |
| abstract_inverted_index.fractional | 160 |
| abstract_inverted_index.similarity | 55, 58, 61, 66, 70, 98, 123, 187, 194 |
| abstract_inverted_index.coefficient | 153 |
| abstract_inverted_index.correlation | 202 |
| abstract_inverted_index.represented | 26 |
| abstract_inverted_index.similarity. | 73, 171, 216 |
| abstract_inverted_index.demonstrates | 219 |
| abstract_inverted_index.interactions | 13 |
| abstract_inverted_index.neighborhood | 65 |
| abstract_inverted_index.relationship | 33 |
| abstract_inverted_index.respectively | 259 |
| abstract_inverted_index.strategizing | 107 |
| abstract_inverted_index.investigating | 11 |
| abstract_inverted_index.maximization, | 111 |
| abstract_inverted_index.TRANSPORTATION | 257 |
| abstract_inverted_index.controllability, | 113 |
| abstract_inverted_index.non-correlation) | 205 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.59923158 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |