Heterogeneous Multi-Agent Communication Learning via Graph Information Maximization Article Swipe
Communication learning is an effective way to solve complicated cooperative tasks in multi-agent reinforcement learning (MARL) domain.Graph neural network (GNN) has been widely adopt for learning the multi-agent communication and various GNN-based MARL methods have emerged.However, most of these methods are not specially designed for heterogeneous multi-agent scenarios, where agents have heterogeneous attributes or features based on different observation spaces or action sets.Without effective processing and transmission of heterogeneous feature information, communication learning will be useless and even reduce the performance of cooperation.To solve this problem, we propose a communication learning mechanism based on heterogeneous GNN and graph information maximization to learn effective communication for heterogeneous agents.Specifically, we use heterogeneous GNN for learning the efficient message representations, which aggregate the local feature information of neighboring agents.Furthermore, we maximize the mutual information (MI) between message representations and local values to make efficient use of information.Besides, we present a MARL framework that can flexibly integrate the proposed communication mechanism with existing value factorization methods.Experiments on various heterogeneous multi-agent scenarios demonstrate the effectiveness and superiority of the proposed method compared with baselines.
Related Topics
Concepts
Computer science
Maximization
Graph
Reinforcement learning
Feature learning
Artificial intelligence
Information exchange
Distributed computing
Message passing
Heterogeneous network
Machine learning
Theoretical computer science
Wireless
Mathematical optimization
Wireless network
Telecommunications
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.18293/seke2023-099
- https://doi.org/10.18293/seke2023-099
- OA Status
- bronze
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386365999
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386365999Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18293/seke2023-099Digital Object Identifier
- Title
-
Heterogeneous Multi-Agent Communication Learning via Graph Information MaximizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-01Full publication date if available
- Authors
-
Du Wei, Shifei DingList of authors in order
- Landing page
-
https://doi.org/10.18293/seke2023-099Publisher landing page
- PDF URL
-
https://doi.org/10.18293/seke2023-099Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.18293/seke2023-099Direct OA link when available
- Concepts
-
Computer science, Maximization, Graph, Reinforcement learning, Feature learning, Artificial intelligence, Information exchange, Distributed computing, Message passing, Heterogeneous network, Machine learning, Theoretical computer science, Wireless, Mathematical optimization, Wireless network, Telecommunications, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386365999 |
|---|---|
| doi | https://doi.org/10.18293/seke2023-099 |
| ids.doi | https://doi.org/10.18293/seke2023-099 |
| ids.openalex | https://openalex.org/W4386365999 |
| fwci | 0.0 |
| type | article |
| title | Heterogeneous Multi-Agent Communication Learning via Graph Information Maximization |
| biblio.issue | |
| biblio.volume | 2023 |
| biblio.last_page | 473 |
| biblio.first_page | 468 |
| 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 | 0.9976999759674072 |
| 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/T10064 |
| topics[1].field.id | https://openalex.org/fields/31 |
| topics[1].field.display_name | Physics and Astronomy |
| topics[1].score | 0.9718999862670898 |
| 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 | Complex Network Analysis Techniques |
| topics[2].id | https://openalex.org/T10462 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.965399980545044 |
| 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 | Reinforcement Learning in Robotics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8353226184844971 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2776330181 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5814912915229797 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q18358244 |
| concepts[1].display_name | Maximization |
| concepts[2].id | https://openalex.org/C132525143 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5653882622718811 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[2].display_name | Graph |
| concepts[3].id | https://openalex.org/C97541855 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5432506799697876 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[3].display_name | Reinforcement learning |
| concepts[4].id | https://openalex.org/C59404180 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4888133704662323 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17013334 |
| concepts[4].display_name | Feature learning |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.48325639963150024 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C189693848 |
| concepts[6].level | 2 |
| concepts[6].score | 0.45185011625289917 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6031064 |
| concepts[6].display_name | Information exchange |
| concepts[7].id | https://openalex.org/C120314980 |
| concepts[7].level | 1 |
| concepts[7].score | 0.450111985206604 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q180634 |
| concepts[7].display_name | Distributed computing |
| concepts[8].id | https://openalex.org/C854659 |
| concepts[8].level | 2 |
| concepts[8].score | 0.43422675132751465 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1859284 |
| concepts[8].display_name | Message passing |
| concepts[9].id | https://openalex.org/C158207573 |
| concepts[9].level | 4 |
| concepts[9].score | 0.4329163432121277 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q5747224 |
| concepts[9].display_name | Heterogeneous network |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.42165204882621765 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C80444323 |
| concepts[11].level | 1 |
| concepts[11].score | 0.36707836389541626 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[11].display_name | Theoretical computer science |
| concepts[12].id | https://openalex.org/C555944384 |
| concepts[12].level | 2 |
| concepts[12].score | 0.21236932277679443 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q249 |
| concepts[12].display_name | Wireless |
| concepts[13].id | https://openalex.org/C126255220 |
| concepts[13].level | 1 |
| concepts[13].score | 0.13673889636993408 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[13].display_name | Mathematical optimization |
| concepts[14].id | https://openalex.org/C108037233 |
| concepts[14].level | 3 |
| concepts[14].score | 0.12960848212242126 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11375 |
| concepts[14].display_name | Wireless network |
| concepts[15].id | https://openalex.org/C76155785 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[15].display_name | Telecommunications |
| concepts[16].id | https://openalex.org/C33923547 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[16].display_name | Mathematics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8353226184844971 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/maximization |
| keywords[1].score | 0.5814912915229797 |
| keywords[1].display_name | Maximization |
| keywords[2].id | https://openalex.org/keywords/graph |
| keywords[2].score | 0.5653882622718811 |
| keywords[2].display_name | Graph |
| keywords[3].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[3].score | 0.5432506799697876 |
| keywords[3].display_name | Reinforcement learning |
| keywords[4].id | https://openalex.org/keywords/feature-learning |
| keywords[4].score | 0.4888133704662323 |
| keywords[4].display_name | Feature learning |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.48325639963150024 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/information-exchange |
| keywords[6].score | 0.45185011625289917 |
| keywords[6].display_name | Information exchange |
| keywords[7].id | https://openalex.org/keywords/distributed-computing |
| keywords[7].score | 0.450111985206604 |
| keywords[7].display_name | Distributed computing |
| keywords[8].id | https://openalex.org/keywords/message-passing |
| keywords[8].score | 0.43422675132751465 |
| keywords[8].display_name | Message passing |
| keywords[9].id | https://openalex.org/keywords/heterogeneous-network |
| keywords[9].score | 0.4329163432121277 |
| keywords[9].display_name | Heterogeneous network |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.42165204882621765 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[11].score | 0.36707836389541626 |
| keywords[11].display_name | Theoretical computer science |
| keywords[12].id | https://openalex.org/keywords/wireless |
| keywords[12].score | 0.21236932277679443 |
| keywords[12].display_name | Wireless |
| keywords[13].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[13].score | 0.13673889636993408 |
| keywords[13].display_name | Mathematical optimization |
| keywords[14].id | https://openalex.org/keywords/wireless-network |
| keywords[14].score | 0.12960848212242126 |
| keywords[14].display_name | Wireless network |
| language | en |
| locations[0].id | doi:10.18293/seke2023-099 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4220650826 |
| locations[0].source.issn | 2325-9000, 2325-9086 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2325-9000 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://doi.org/10.18293/seke2023-099 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Conferences on Software Engineering and Knowledge Engineering |
| locations[0].landing_page_url | http://doi.org/10.18293/seke2023-099 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5100572979 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1215-2223 |
| authorships[0].author.display_name | Du Wei |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4390039265 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science and Technology |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I25757504 |
| authorships[0].affiliations[1].raw_affiliation_string | China University of Mining and Technology Xuzhou, China |
| authorships[0].institutions[0].id | https://openalex.org/I4390039265 |
| authorships[0].institutions[0].ror | https://ror.org/02sr2ee22 |
| authorships[0].institutions[0].type | company |
| authorships[0].institutions[0].lineage | https://openalex.org/I4390039265 |
| authorships[0].institutions[0].country_code | |
| authorships[0].institutions[0].display_name | PRG S&Tech (South Korea) |
| authorships[0].institutions[1].id | https://openalex.org/I25757504 |
| authorships[0].institutions[1].ror | https://ror.org/01xt2dr21 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I25757504 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | China University of Mining and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wei Du |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | China University of Mining and Technology Xuzhou, China, School of Computer Science and Technology |
| authorships[1].author.id | https://openalex.org/A5082274996 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1391-2717 |
| authorships[1].author.display_name | Shifei Ding |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I25757504 |
| authorships[1].affiliations[0].raw_affiliation_string | China University of Mining and Technology Xuzhou, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I4390039265 |
| authorships[1].affiliations[1].raw_affiliation_string | School of Computer Science and Technology |
| authorships[1].institutions[0].id | https://openalex.org/I4390039265 |
| authorships[1].institutions[0].ror | https://ror.org/02sr2ee22 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I4390039265 |
| authorships[1].institutions[0].country_code | |
| authorships[1].institutions[0].display_name | PRG S&Tech (South Korea) |
| authorships[1].institutions[1].id | https://openalex.org/I25757504 |
| authorships[1].institutions[1].ror | https://ror.org/01xt2dr21 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I25757504 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | China University of Mining and Technology |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Shifei Ding |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | China University of Mining and Technology Xuzhou, China, School of Computer Science and Technology |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.18293/seke2023-099 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Heterogeneous Multi-Agent Communication Learning via Graph Information Maximization |
| has_fulltext | True |
| 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 | 0.9976999759674072 |
| 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/W2978729728, https://openalex.org/W1927327903, https://openalex.org/W1544665982, https://openalex.org/W2022479666, https://openalex.org/W4288966080, https://openalex.org/W2805504328, https://openalex.org/W4248847438, https://openalex.org/W2509090697, https://openalex.org/W1953159635, https://openalex.org/W4238131711 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.18293/seke2023-099 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4220650826 |
| best_oa_location.source.issn | 2325-9000, 2325-9086 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2325-9000 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://doi.org/10.18293/seke2023-099 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Conferences on Software Engineering and Knowledge Engineering |
| best_oa_location.landing_page_url | http://doi.org/10.18293/seke2023-099 |
| primary_location.id | doi:10.18293/seke2023-099 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4220650826 |
| primary_location.source.issn | 2325-9000, 2325-9086 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2325-9000 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://doi.org/10.18293/seke2023-099 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Conferences on Software Engineering and Knowledge Engineering |
| primary_location.landing_page_url | http://doi.org/10.18293/seke2023-099 |
| publication_date | 2023-07-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2908261578, https://openalex.org/W3106649810, https://openalex.org/W2794643322, https://openalex.org/W6781750019, https://openalex.org/W2402402867, https://openalex.org/W2803155336, https://openalex.org/W3081632678, https://openalex.org/W2896451037, https://openalex.org/W6755542948, https://openalex.org/W2996525917, https://openalex.org/W2975210693, https://openalex.org/W2991479173, https://openalex.org/W3173215816, https://openalex.org/W3088335680, https://openalex.org/W3195014486, https://openalex.org/W6679726148, https://openalex.org/W6758846586, https://openalex.org/W2997940961, https://openalex.org/W4298162725, https://openalex.org/W2998367975, https://openalex.org/W3046288222, https://openalex.org/W4289363497, https://openalex.org/W2133299088, https://openalex.org/W2964338167, https://openalex.org/W4288594419, https://openalex.org/W4287022326, https://openalex.org/W4295598622 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 88, 146 |
| abstract_inverted_index.an | 3 |
| abstract_inverted_index.be | 74 |
| abstract_inverted_index.in | 11 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 37, 67, 81, 123, 142, 172 |
| abstract_inverted_index.on | 56, 93, 162 |
| abstract_inverted_index.or | 53, 60 |
| abstract_inverted_index.to | 6, 100, 138 |
| abstract_inverted_index.we | 86, 107, 126, 144 |
| abstract_inverted_index.GNN | 95, 110 |
| abstract_inverted_index.and | 29, 65, 76, 96, 135, 170 |
| abstract_inverted_index.are | 40 |
| abstract_inverted_index.can | 150 |
| abstract_inverted_index.for | 24, 44, 104, 111 |
| abstract_inverted_index.has | 20 |
| abstract_inverted_index.not | 41 |
| abstract_inverted_index.the | 26, 79, 113, 119, 128, 153, 168, 173 |
| abstract_inverted_index.use | 108, 141 |
| abstract_inverted_index.way | 5 |
| abstract_inverted_index.(MI) | 131 |
| abstract_inverted_index.MARL | 32, 147 |
| abstract_inverted_index.been | 21 |
| abstract_inverted_index.even | 77 |
| abstract_inverted_index.have | 34, 50 |
| abstract_inverted_index.make | 139 |
| abstract_inverted_index.most | 36 |
| abstract_inverted_index.that | 149 |
| abstract_inverted_index.this | 84 |
| abstract_inverted_index.will | 73 |
| abstract_inverted_index.with | 157, 177 |
| abstract_inverted_index.(GNN) | 19 |
| abstract_inverted_index.adopt | 23 |
| abstract_inverted_index.based | 55, 92 |
| abstract_inverted_index.graph | 97 |
| abstract_inverted_index.learn | 101 |
| abstract_inverted_index.local | 120, 136 |
| abstract_inverted_index.solve | 7, 83 |
| abstract_inverted_index.tasks | 10 |
| abstract_inverted_index.these | 38 |
| abstract_inverted_index.value | 159 |
| abstract_inverted_index.where | 48 |
| abstract_inverted_index.which | 117 |
| abstract_inverted_index.(MARL) | 15 |
| abstract_inverted_index.action | 61 |
| abstract_inverted_index.agents | 49 |
| abstract_inverted_index.method | 175 |
| abstract_inverted_index.mutual | 129 |
| abstract_inverted_index.neural | 17 |
| abstract_inverted_index.reduce | 78 |
| abstract_inverted_index.spaces | 59 |
| abstract_inverted_index.values | 137 |
| abstract_inverted_index.widely | 22 |
| abstract_inverted_index.between | 132 |
| abstract_inverted_index.feature | 69, 121 |
| abstract_inverted_index.message | 115, 133 |
| abstract_inverted_index.methods | 33, 39 |
| abstract_inverted_index.network | 18 |
| abstract_inverted_index.present | 145 |
| abstract_inverted_index.propose | 87 |
| abstract_inverted_index.useless | 75 |
| abstract_inverted_index.various | 30, 163 |
| abstract_inverted_index.compared | 176 |
| abstract_inverted_index.designed | 43 |
| abstract_inverted_index.existing | 158 |
| abstract_inverted_index.features | 54 |
| abstract_inverted_index.flexibly | 151 |
| abstract_inverted_index.learning | 1, 14, 25, 72, 90, 112 |
| abstract_inverted_index.maximize | 127 |
| abstract_inverted_index.problem, | 85 |
| abstract_inverted_index.proposed | 154, 174 |
| abstract_inverted_index.GNN-based | 31 |
| abstract_inverted_index.aggregate | 118 |
| abstract_inverted_index.different | 57 |
| abstract_inverted_index.effective | 4, 63, 102 |
| abstract_inverted_index.efficient | 114, 140 |
| abstract_inverted_index.framework | 148 |
| abstract_inverted_index.integrate | 152 |
| abstract_inverted_index.mechanism | 91, 156 |
| abstract_inverted_index.scenarios | 166 |
| abstract_inverted_index.specially | 42 |
| abstract_inverted_index.attributes | 52 |
| abstract_inverted_index.baselines. | 178 |
| abstract_inverted_index.processing | 64 |
| abstract_inverted_index.scenarios, | 47 |
| abstract_inverted_index.complicated | 8 |
| abstract_inverted_index.cooperative | 9 |
| abstract_inverted_index.demonstrate | 167 |
| abstract_inverted_index.information | 98, 122, 130 |
| abstract_inverted_index.multi-agent | 12, 27, 46, 165 |
| abstract_inverted_index.neighboring | 124 |
| abstract_inverted_index.observation | 58 |
| abstract_inverted_index.performance | 80 |
| abstract_inverted_index.superiority | 171 |
| abstract_inverted_index.domain.Graph | 16 |
| abstract_inverted_index.information, | 70 |
| abstract_inverted_index.maximization | 99 |
| abstract_inverted_index.sets.Without | 62 |
| abstract_inverted_index.transmission | 66 |
| abstract_inverted_index.Communication | 0 |
| abstract_inverted_index.communication | 28, 71, 89, 103, 155 |
| abstract_inverted_index.effectiveness | 169 |
| abstract_inverted_index.factorization | 160 |
| abstract_inverted_index.heterogeneous | 45, 51, 68, 94, 105, 109, 164 |
| abstract_inverted_index.reinforcement | 13 |
| abstract_inverted_index.cooperation.To | 82 |
| abstract_inverted_index.representations | 134 |
| abstract_inverted_index.emerged.However, | 35 |
| abstract_inverted_index.representations, | 116 |
| abstract_inverted_index.agents.Furthermore, | 125 |
| abstract_inverted_index.methods.Experiments | 161 |
| abstract_inverted_index.agents.Specifically, | 106 |
| abstract_inverted_index.information.Besides, | 143 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5082274996 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I25757504, https://openalex.org/I4390039265 |
| citation_normalized_percentile.value | 0.12538524 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |