Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictors Article Swipe
Yongjian He
,
Yijun Ran
,
Zengru Di
,
Tao Zhou
,
Xiaoke Xu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.09406
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.09406
Link prediction has become a critical problem in network science and has thus attracted increasing research interest. Popularity and similarity are two primary mechanisms in the formation of real networks. However, the roles of popularity and similarity mechanisms in link prediction across various domain networks remain poorly understood. Accordingly, this study used orbit degrees of graphlets to construct multi-order popularity- and similarity-based network link predictors, demonstrating that traditional popularity- and similarity-based indices can be efficiently represented in terms of orbit degrees. Moreover, we designed a supervised learning model that fuses multiple orbit-degree-based features and validated its link prediction performance. We also evaluated the mean absolute Shapley additive explanations of each feature within this model across 550 real-world networks from six domains. We observed that the homophily mechanism, which is a similarity-based feature, dominated social networks, with its win rate being 91\%. Moreover, a different similarity-based feature was prominent in economic, technological, and information networks. Finally, no single feature dominated the biological and transportation networks. The proposed approach improves the accuracy and interpretability of link prediction, thus facilitating the analysis of complex networks.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.09406
- https://arxiv.org/pdf/2408.09406
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402528218
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4402528218Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2408.09406Digital Object Identifier
- Title
-
Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictorsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-18Full publication date if available
- Authors
-
Yongjian He, Yijun Ran, Zengru Di, Tao Zhou, Xiaoke XuList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.09406Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2408.09406Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2408.09406Direct OA link when available
- Concepts
-
Popularity, Link (geometry), Similarity (geometry), Order (exchange), Computer science, Data mining, Artificial intelligence, Psychology, Business, Social psychology, Computer network, Finance, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4402528218 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2408.09406 |
| ids.doi | https://doi.org/10.48550/arxiv.2408.09406 |
| ids.openalex | https://openalex.org/W4402528218 |
| fwci | |
| type | preprint |
| title | Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictors |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9986000061035156 |
| 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/T11273 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9876000285148621 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Advanced Graph Neural Networks |
| topics[2].id | https://openalex.org/T13976 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9596999883651733 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1710 |
| topics[2].subfield.display_name | Information Systems |
| topics[2].display_name | Web visibility and informetrics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2780586970 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8069828748703003 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1357284 |
| concepts[0].display_name | Popularity |
| concepts[1].id | https://openalex.org/C2778753846 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7720866203308105 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q6554239 |
| concepts[1].display_name | Link (geometry) |
| concepts[2].id | https://openalex.org/C103278499 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6066067814826965 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q254465 |
| concepts[2].display_name | Similarity (geometry) |
| concepts[3].id | https://openalex.org/C182306322 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5913125872612 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1779371 |
| concepts[3].display_name | Order (exchange) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.49741533398628235 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C124101348 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3801535964012146 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[5].display_name | Data mining |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3103693127632141 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C15744967 |
| concepts[7].level | 0 |
| concepts[7].score | 0.23099398612976074 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[7].display_name | Psychology |
| concepts[8].id | https://openalex.org/C144133560 |
| concepts[8].level | 0 |
| concepts[8].score | 0.20039519667625427 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q4830453 |
| concepts[8].display_name | Business |
| concepts[9].id | https://openalex.org/C77805123 |
| concepts[9].level | 1 |
| concepts[9].score | 0.09074732661247253 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q161272 |
| concepts[9].display_name | Social psychology |
| concepts[10].id | https://openalex.org/C31258907 |
| concepts[10].level | 1 |
| concepts[10].score | 0.05779770016670227 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[10].display_name | Computer network |
| concepts[11].id | https://openalex.org/C10138342 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q43015 |
| concepts[11].display_name | Finance |
| concepts[12].id | https://openalex.org/C115961682 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[12].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/popularity |
| keywords[0].score | 0.8069828748703003 |
| keywords[0].display_name | Popularity |
| keywords[1].id | https://openalex.org/keywords/link |
| keywords[1].score | 0.7720866203308105 |
| keywords[1].display_name | Link (geometry) |
| keywords[2].id | https://openalex.org/keywords/similarity |
| keywords[2].score | 0.6066067814826965 |
| keywords[2].display_name | Similarity (geometry) |
| keywords[3].id | https://openalex.org/keywords/order |
| keywords[3].score | 0.5913125872612 |
| keywords[3].display_name | Order (exchange) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.49741533398628235 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/data-mining |
| keywords[5].score | 0.3801535964012146 |
| keywords[5].display_name | Data mining |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.3103693127632141 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/psychology |
| keywords[7].score | 0.23099398612976074 |
| keywords[7].display_name | Psychology |
| keywords[8].id | https://openalex.org/keywords/business |
| keywords[8].score | 0.20039519667625427 |
| keywords[8].display_name | Business |
| keywords[9].id | https://openalex.org/keywords/social-psychology |
| keywords[9].score | 0.09074732661247253 |
| keywords[9].display_name | Social psychology |
| keywords[10].id | https://openalex.org/keywords/computer-network |
| keywords[10].score | 0.05779770016670227 |
| keywords[10].display_name | Computer network |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2408.09406 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2408.09406 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2408.09406 |
| locations[1].id | doi:10.48550/arxiv.2408.09406 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2408.09406 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5100567309 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Yongjian He |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | He, Yong-Jian |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5084577409 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7047-3343 |
| authorships[1].author.display_name | Yijun Ran |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ran, Yijun |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5102710973 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5240-298X |
| authorships[2].author.display_name | Zengru Di |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Di, Zengru |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5090925242 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0561-2316 |
| authorships[3].author.display_name | Tao Zhou |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zhou, Tao |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5112582118 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Xiaoke Xu |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Xu, Xiao-Ke |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2408.09406 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-09-14T00:00:00 |
| display_name | Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictors |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9986000061035156 |
| 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/W2368605798, https://openalex.org/W2518037665, https://openalex.org/W2348524959, https://openalex.org/W2477036161, https://openalex.org/W2368049389, https://openalex.org/W2384861574, https://openalex.org/W4294565801, https://openalex.org/W2170801710, https://openalex.org/W2952704802, https://openalex.org/W1787322086 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2408.09406 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2408.09406 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2408.09406 |
| primary_location.id | pmh:oai:arXiv.org:2408.09406 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2408.09406 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2408.09406 |
| publication_date | 2024-08-18 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 4, 84, 129, 142 |
| abstract_inverted_index.We | 99, 121 |
| abstract_inverted_index.be | 73 |
| abstract_inverted_index.in | 7, 24, 38, 76, 148 |
| abstract_inverted_index.is | 128 |
| abstract_inverted_index.no | 155 |
| abstract_inverted_index.of | 27, 33, 54, 78, 108, 172, 179 |
| abstract_inverted_index.to | 56 |
| abstract_inverted_index.we | 82 |
| abstract_inverted_index.550 | 115 |
| abstract_inverted_index.The | 164 |
| abstract_inverted_index.and | 10, 18, 35, 60, 69, 93, 151, 161, 170 |
| abstract_inverted_index.are | 20 |
| abstract_inverted_index.can | 72 |
| abstract_inverted_index.has | 2, 11 |
| abstract_inverted_index.its | 95, 136 |
| abstract_inverted_index.six | 119 |
| abstract_inverted_index.the | 25, 31, 102, 124, 159, 168, 177 |
| abstract_inverted_index.two | 21 |
| abstract_inverted_index.was | 146 |
| abstract_inverted_index.win | 137 |
| abstract_inverted_index.Link | 0 |
| abstract_inverted_index.also | 100 |
| abstract_inverted_index.each | 109 |
| abstract_inverted_index.from | 118 |
| abstract_inverted_index.link | 39, 63, 96, 173 |
| abstract_inverted_index.mean | 103 |
| abstract_inverted_index.rate | 138 |
| abstract_inverted_index.real | 28 |
| abstract_inverted_index.that | 66, 88, 123 |
| abstract_inverted_index.this | 49, 112 |
| abstract_inverted_index.thus | 12, 175 |
| abstract_inverted_index.used | 51 |
| abstract_inverted_index.with | 135 |
| abstract_inverted_index.91\%. | 140 |
| abstract_inverted_index.being | 139 |
| abstract_inverted_index.fuses | 89 |
| abstract_inverted_index.model | 87, 113 |
| abstract_inverted_index.orbit | 52, 79 |
| abstract_inverted_index.roles | 32 |
| abstract_inverted_index.study | 50 |
| abstract_inverted_index.terms | 77 |
| abstract_inverted_index.which | 127 |
| abstract_inverted_index.across | 41, 114 |
| abstract_inverted_index.become | 3 |
| abstract_inverted_index.domain | 43 |
| abstract_inverted_index.poorly | 46 |
| abstract_inverted_index.remain | 45 |
| abstract_inverted_index.single | 156 |
| abstract_inverted_index.social | 133 |
| abstract_inverted_index.within | 111 |
| abstract_inverted_index.Shapley | 105 |
| abstract_inverted_index.complex | 180 |
| abstract_inverted_index.degrees | 53 |
| abstract_inverted_index.feature | 110, 145, 157 |
| abstract_inverted_index.indices | 71 |
| abstract_inverted_index.network | 8, 62 |
| abstract_inverted_index.primary | 22 |
| abstract_inverted_index.problem | 6 |
| abstract_inverted_index.science | 9 |
| abstract_inverted_index.various | 42 |
| abstract_inverted_index.Finally, | 154 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.absolute | 104 |
| abstract_inverted_index.accuracy | 169 |
| abstract_inverted_index.additive | 106 |
| abstract_inverted_index.analysis | 178 |
| abstract_inverted_index.approach | 166 |
| abstract_inverted_index.critical | 5 |
| abstract_inverted_index.degrees. | 80 |
| abstract_inverted_index.designed | 83 |
| abstract_inverted_index.domains. | 120 |
| abstract_inverted_index.feature, | 131 |
| abstract_inverted_index.features | 92 |
| abstract_inverted_index.improves | 167 |
| abstract_inverted_index.learning | 86 |
| abstract_inverted_index.multiple | 90 |
| abstract_inverted_index.networks | 44, 117 |
| abstract_inverted_index.observed | 122 |
| abstract_inverted_index.proposed | 165 |
| abstract_inverted_index.research | 15 |
| abstract_inverted_index.Moreover, | 81, 141 |
| abstract_inverted_index.attracted | 13 |
| abstract_inverted_index.construct | 57 |
| abstract_inverted_index.different | 143 |
| abstract_inverted_index.dominated | 132, 158 |
| abstract_inverted_index.economic, | 149 |
| abstract_inverted_index.evaluated | 101 |
| abstract_inverted_index.formation | 26 |
| abstract_inverted_index.graphlets | 55 |
| abstract_inverted_index.homophily | 125 |
| abstract_inverted_index.interest. | 16 |
| abstract_inverted_index.networks, | 134 |
| abstract_inverted_index.networks. | 29, 153, 163, 181 |
| abstract_inverted_index.prominent | 147 |
| abstract_inverted_index.validated | 94 |
| abstract_inverted_index.Popularity | 17 |
| abstract_inverted_index.biological | 160 |
| abstract_inverted_index.increasing | 14 |
| abstract_inverted_index.mechanism, | 126 |
| abstract_inverted_index.mechanisms | 23, 37 |
| abstract_inverted_index.popularity | 34 |
| abstract_inverted_index.prediction | 1, 40, 97 |
| abstract_inverted_index.real-world | 116 |
| abstract_inverted_index.similarity | 19, 36 |
| abstract_inverted_index.supervised | 85 |
| abstract_inverted_index.efficiently | 74 |
| abstract_inverted_index.information | 152 |
| abstract_inverted_index.multi-order | 58 |
| abstract_inverted_index.popularity- | 59, 68 |
| abstract_inverted_index.prediction, | 174 |
| abstract_inverted_index.predictors, | 64 |
| abstract_inverted_index.represented | 75 |
| abstract_inverted_index.traditional | 67 |
| abstract_inverted_index.understood. | 47 |
| abstract_inverted_index.Accordingly, | 48 |
| abstract_inverted_index.explanations | 107 |
| abstract_inverted_index.facilitating | 176 |
| abstract_inverted_index.performance. | 98 |
| abstract_inverted_index.demonstrating | 65 |
| abstract_inverted_index.technological, | 150 |
| abstract_inverted_index.transportation | 162 |
| abstract_inverted_index.interpretability | 171 |
| abstract_inverted_index.similarity-based | 61, 70, 130, 144 |
| abstract_inverted_index.orbit-degree-based | 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |