A Structure-aware Invariant Learning Framework for Node-level Graph OOD Generalization Article Swipe
Ruiwen Yuan
,
Yongqiang Tang
,
Wensheng Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3690624.3709227
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3690624.3709227
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3690624.3709227
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409149633
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4409149633Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3690624.3709227Digital Object Identifier
- Title
-
A Structure-aware Invariant Learning Framework for Node-level Graph OOD GeneralizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-04Full publication date if available
- Authors
-
Ruiwen Yuan, Yongqiang Tang, Wensheng ZhangList of authors in order
- Landing page
-
https://doi.org/10.1145/3690624.3709227Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1145/3690624.3709227Direct OA link when available
- Concepts
-
Computer science, Invariant (physics), Generalization, Graph, Theoretical computer science, Artificial intelligence, Mathematics, Mathematical physics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4409149633 |
|---|---|
| doi | https://doi.org/10.1145/3690624.3709227 |
| ids.doi | https://doi.org/10.1145/3690624.3709227 |
| ids.openalex | https://openalex.org/W4409149633 |
| fwci | 0.0 |
| type | article |
| title | A Structure-aware Invariant Learning Framework for Node-level Graph OOD Generalization |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 1890 |
| biblio.first_page | 1879 |
| topics[0].id | https://openalex.org/T11273 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Advanced Graph Neural Networks |
| topics[1].id | https://openalex.org/T10203 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9966999888420105 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1710 |
| topics[1].subfield.display_name | Information Systems |
| topics[1].display_name | Recommender Systems and Techniques |
| topics[2].id | https://openalex.org/T10637 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9846000075340271 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Advanced Clustering Algorithms Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6481673717498779 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C190470478 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5993323922157288 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2370229 |
| concepts[1].display_name | Invariant (physics) |
| concepts[2].id | https://openalex.org/C177148314 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5623462200164795 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[2].display_name | Generalization |
| concepts[3].id | https://openalex.org/C132525143 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4956926107406616 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[3].display_name | Graph |
| concepts[4].id | https://openalex.org/C80444323 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4374505579471588 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[4].display_name | Theoretical computer science |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4124659299850464 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C33923547 |
| concepts[6].level | 0 |
| concepts[6].score | 0.21481719613075256 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[6].display_name | Mathematics |
| concepts[7].id | https://openalex.org/C37914503 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q156495 |
| concepts[7].display_name | Mathematical physics |
| concepts[8].id | https://openalex.org/C134306372 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[8].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6481673717498779 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/invariant |
| keywords[1].score | 0.5993323922157288 |
| keywords[1].display_name | Invariant (physics) |
| keywords[2].id | https://openalex.org/keywords/generalization |
| keywords[2].score | 0.5623462200164795 |
| keywords[2].display_name | Generalization |
| keywords[3].id | https://openalex.org/keywords/graph |
| keywords[3].score | 0.4956926107406616 |
| keywords[3].display_name | Graph |
| keywords[4].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[4].score | 0.4374505579471588 |
| keywords[4].display_name | Theoretical computer science |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4124659299850464 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/mathematics |
| keywords[6].score | 0.21481719613075256 |
| keywords[6].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1145/3690624.3709227 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-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 | Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 |
| locations[0].landing_page_url | https://doi.org/10.1145/3690624.3709227 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5024939023 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4649-0167 |
| authorships[0].author.display_name | Ruiwen Yuan |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210112150, https://openalex.org/I4210165038 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Chinese Academy of Sciences, Beijing, China and Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210112150 |
| authorships[0].institutions[0].ror | https://ror.org/022c3hy66 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210112150 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Institute of Automation |
| authorships[0].institutions[1].id | https://openalex.org/I4210165038 |
| authorships[0].institutions[1].ror | https://ror.org/05qbk4x57 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | University of Chinese Academy of Sciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ruiwen Yuan |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of Chinese Academy of Sciences, Beijing, China and Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| authorships[1].author.id | https://openalex.org/A5109012722 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9333-8200 |
| authorships[1].author.display_name | Yongqiang Tang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210094879 |
| authorships[1].affiliations[0].raw_affiliation_string | Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| authorships[1].institutions[0].id | https://openalex.org/I19820366 |
| authorships[1].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[1].institutions[0].type | government |
| authorships[1].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[1].institutions[1].id | https://openalex.org/I4210094879 |
| authorships[1].institutions[1].ror | https://ror.org/00qdtba35 |
| authorships[1].institutions[1].type | facility |
| authorships[1].institutions[1].lineage | https://openalex.org/I4210094879, https://openalex.org/I4210142748 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Shandong Institute of Automation |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yongqiang Tang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| authorships[2].author.id | https://openalex.org/A5100414787 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0752-941X |
| authorships[2].author.display_name | Wensheng Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I37987034, https://openalex.org/I4210112150 |
| authorships[2].affiliations[0].raw_affiliation_string | Guangzhou University, Guangzhou, China and Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| authorships[2].institutions[0].id | https://openalex.org/I19820366 |
| authorships[2].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[2].institutions[1].id | https://openalex.org/I37987034 |
| authorships[2].institutions[1].ror | https://ror.org/05ar8rn06 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I37987034 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Guangzhou University |
| authorships[2].institutions[2].id | https://openalex.org/I4210112150 |
| authorships[2].institutions[2].ror | https://ror.org/022c3hy66 |
| authorships[2].institutions[2].type | facility |
| authorships[2].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210112150 |
| authorships[2].institutions[2].country_code | CN |
| authorships[2].institutions[2].display_name | Institute of Automation |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Wensheng Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Guangzhou University, Guangzhou, China and Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1145/3690624.3709227 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Structure-aware Invariant Learning Framework for Node-level Graph OOD Generalization |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11273 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Advanced Graph Neural Networks |
| related_works | https://openalex.org/W3162204513, https://openalex.org/W2371138613, https://openalex.org/W2048963458, https://openalex.org/W43109613, https://openalex.org/W2359952343, https://openalex.org/W2239445980, https://openalex.org/W2080152487, https://openalex.org/W3083152911, https://openalex.org/W3022347918, https://openalex.org/W4200527723 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1145/3690624.3709227 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-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 | Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 |
| best_oa_location.landing_page_url | https://doi.org/10.1145/3690624.3709227 |
| primary_location.id | doi:10.1145/3690624.3709227 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-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 | Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 |
| primary_location.landing_page_url | https://doi.org/10.1145/3690624.3709227 |
| publication_date | 2025-04-04 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3206097584, https://openalex.org/W4304984779, https://openalex.org/W4320476363, https://openalex.org/W6637618735, https://openalex.org/W4385973160, https://openalex.org/W6839216370, https://openalex.org/W2903871660, https://openalex.org/W3045200674, https://openalex.org/W6600291067, https://openalex.org/W4385565521, https://openalex.org/W6792108999, https://openalex.org/W4221166060, https://openalex.org/W2998313947, https://openalex.org/W3160872503, https://openalex.org/W4290948450, https://openalex.org/W2964288524, https://openalex.org/W4312908248, https://openalex.org/W3172710079, https://openalex.org/W3186377753, https://openalex.org/W4396735935, https://openalex.org/W6784376221, https://openalex.org/W4312347724, https://openalex.org/W4386072181, https://openalex.org/W3188978989, https://openalex.org/W2990138404, https://openalex.org/W3214511341, https://openalex.org/W4396758529, https://openalex.org/W2914304175, https://openalex.org/W4297969478, https://openalex.org/W4283204295, https://openalex.org/W3134210100 |
| referenced_works_count | 31 |
| abstract_inverted_index | |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.043542 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |