An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures Article Swipe
Shicheng Li
,
Bharat Singhal
,
Jr-Shin Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.15990
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.15990
In complex networks, interactions between multiple agents give rise to an array of intricate global dynamics, ranging from synchronization to cluster formations. Decoding the connectivity structure as well as the types of interactions from measurement data is the first step toward understanding these intriguing behaviors. In this paper, we present a bilinear optimization framework to infer both the connectivity and interaction functions of oscillator networks with the identical class of coupling functions. We then propose an iterative algorithm to solve the resulting bilinear problem and illustrate its convergence. We validate our approach on both simulated and noisy experimental datasets, where we demonstrate its effectiveness compared with existing approaches.
Related Topics
Concepts
Bilinear interpolation
Decoding methods
Computer science
Inference
Convergence (economics)
Synchronization (alternating current)
Ranging
Coupling (piping)
Cluster (spacecraft)
Theoretical computer science
Algorithm
Artificial intelligence
Channel (broadcasting)
Economics
Mechanical engineering
Engineering
Computer vision
Computer network
Programming language
Economic growth
Telecommunications
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.15990
- https://arxiv.org/pdf/2310.15990
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387947615
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387947615Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2310.15990Digital Object Identifier
- Title
-
An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network StructuresWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-24Full publication date if available
- Authors
-
Shicheng Li, Bharat Singhal, Jr-Shin LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.15990Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.15990Direct 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/2310.15990Direct OA link when available
- Concepts
-
Bilinear interpolation, Decoding methods, Computer science, Inference, Convergence (economics), Synchronization (alternating current), Ranging, Coupling (piping), Cluster (spacecraft), Theoretical computer science, Algorithm, Artificial intelligence, Channel (broadcasting), Economics, Mechanical engineering, Engineering, Computer vision, Computer network, Programming language, Economic growth, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387947615 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2310.15990 |
| ids.doi | https://doi.org/10.48550/arxiv.2310.15990 |
| ids.openalex | https://openalex.org/W4387947615 |
| fwci | |
| type | preprint |
| title | An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10581 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9929999709129333 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Neural dynamics and brain function |
| topics[1].id | https://openalex.org/T10241 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.988099992275238 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2805 |
| topics[1].subfield.display_name | Cognitive Neuroscience |
| topics[1].display_name | Functional Brain Connectivity Studies |
| topics[2].id | https://openalex.org/T11187 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9871000051498413 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1705 |
| topics[2].subfield.display_name | Computer Networks and Communications |
| topics[2].display_name | Nonlinear Dynamics and Pattern Formation |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C205203396 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7863793969154358 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q612143 |
| concepts[0].display_name | Bilinear interpolation |
| concepts[1].id | https://openalex.org/C57273362 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6935860514640808 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q576722 |
| concepts[1].display_name | Decoding methods |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6732224822044373 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2776214188 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6452945470809937 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[3].display_name | Inference |
| concepts[4].id | https://openalex.org/C2777303404 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6328471899032593 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q759757 |
| concepts[4].display_name | Convergence (economics) |
| concepts[5].id | https://openalex.org/C2778562939 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5611523389816284 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1298791 |
| concepts[5].display_name | Synchronization (alternating current) |
| concepts[6].id | https://openalex.org/C115051666 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5538738965988159 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6522493 |
| concepts[6].display_name | Ranging |
| concepts[7].id | https://openalex.org/C131584629 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4716581404209137 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q4308705 |
| concepts[7].display_name | Coupling (piping) |
| concepts[8].id | https://openalex.org/C164866538 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4202979803085327 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q367351 |
| concepts[8].display_name | Cluster (spacecraft) |
| concepts[9].id | https://openalex.org/C80444323 |
| concepts[9].level | 1 |
| concepts[9].score | 0.39491355419158936 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[9].display_name | Theoretical computer science |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3845796287059784 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.21406051516532898 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C127162648 |
| concepts[12].level | 2 |
| concepts[12].score | 0.10778558254241943 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q16858953 |
| concepts[12].display_name | Channel (broadcasting) |
| concepts[13].id | https://openalex.org/C162324750 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[13].display_name | Economics |
| concepts[14].id | https://openalex.org/C78519656 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[14].display_name | Mechanical engineering |
| 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/C31972630 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[16].display_name | Computer vision |
| concepts[17].id | https://openalex.org/C31258907 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[17].display_name | Computer network |
| concepts[18].id | https://openalex.org/C199360897 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[18].display_name | Programming language |
| concepts[19].id | https://openalex.org/C50522688 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q189833 |
| concepts[19].display_name | Economic growth |
| concepts[20].id | https://openalex.org/C76155785 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[20].display_name | Telecommunications |
| keywords[0].id | https://openalex.org/keywords/bilinear-interpolation |
| keywords[0].score | 0.7863793969154358 |
| keywords[0].display_name | Bilinear interpolation |
| keywords[1].id | https://openalex.org/keywords/decoding-methods |
| keywords[1].score | 0.6935860514640808 |
| keywords[1].display_name | Decoding methods |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6732224822044373 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/inference |
| keywords[3].score | 0.6452945470809937 |
| keywords[3].display_name | Inference |
| keywords[4].id | https://openalex.org/keywords/convergence |
| keywords[4].score | 0.6328471899032593 |
| keywords[4].display_name | Convergence (economics) |
| keywords[5].id | https://openalex.org/keywords/synchronization |
| keywords[5].score | 0.5611523389816284 |
| keywords[5].display_name | Synchronization (alternating current) |
| keywords[6].id | https://openalex.org/keywords/ranging |
| keywords[6].score | 0.5538738965988159 |
| keywords[6].display_name | Ranging |
| keywords[7].id | https://openalex.org/keywords/coupling |
| keywords[7].score | 0.4716581404209137 |
| keywords[7].display_name | Coupling (piping) |
| keywords[8].id | https://openalex.org/keywords/cluster |
| keywords[8].score | 0.4202979803085327 |
| keywords[8].display_name | Cluster (spacecraft) |
| keywords[9].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[9].score | 0.39491355419158936 |
| keywords[9].display_name | Theoretical computer science |
| keywords[10].id | https://openalex.org/keywords/algorithm |
| keywords[10].score | 0.3845796287059784 |
| keywords[10].display_name | Algorithm |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.21406051516532898 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/channel |
| keywords[12].score | 0.10778558254241943 |
| keywords[12].display_name | Channel (broadcasting) |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2310.15990 |
| 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/2310.15990 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| 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/2310.15990 |
| locations[1].id | doi:10.48550/arxiv.2310.15990 |
| 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.2310.15990 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5100723940 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5239-6559 |
| authorships[0].author.display_name | Shicheng Li |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Li, Shicheng |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5028623570 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0658-9747 |
| authorships[1].author.display_name | Bharat Singhal |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Singhal, Bharat |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5079314465 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6693-3979 |
| authorships[2].author.display_name | Jr-Shin Li |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Li, Jr-Shin |
| authorships[2].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/2310.15990 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-10-26T00:00:00 |
| display_name | An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10581 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9929999709129333 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Neural dynamics and brain function |
| related_works | https://openalex.org/W2783354812, https://openalex.org/W4384112194, https://openalex.org/W4312958259, https://openalex.org/W2103009189, https://openalex.org/W4308259661, https://openalex.org/W4390813131, https://openalex.org/W2349383066, https://openalex.org/W4328132048, https://openalex.org/W1969901537, https://openalex.org/W2376202349 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2310.15990 |
| 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/2310.15990 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| 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/2310.15990 |
| primary_location.id | pmh:oai:arXiv.org:2310.15990 |
| 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/2310.15990 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| 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/2310.15990 |
| publication_date | 2023-10-24 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 50 |
| abstract_inverted_index.In | 0, 45 |
| abstract_inverted_index.We | 72, 88 |
| abstract_inverted_index.an | 10, 75 |
| abstract_inverted_index.as | 26, 28 |
| abstract_inverted_index.is | 36 |
| abstract_inverted_index.of | 12, 31, 62, 69 |
| abstract_inverted_index.on | 92 |
| abstract_inverted_index.to | 9, 19, 54, 78 |
| abstract_inverted_index.we | 48, 100 |
| abstract_inverted_index.and | 59, 84, 95 |
| abstract_inverted_index.its | 86, 102 |
| abstract_inverted_index.our | 90 |
| abstract_inverted_index.the | 23, 29, 37, 57, 66, 80 |
| abstract_inverted_index.both | 56, 93 |
| abstract_inverted_index.data | 35 |
| abstract_inverted_index.from | 17, 33 |
| abstract_inverted_index.give | 7 |
| abstract_inverted_index.rise | 8 |
| abstract_inverted_index.step | 39 |
| abstract_inverted_index.then | 73 |
| abstract_inverted_index.this | 46 |
| abstract_inverted_index.well | 27 |
| abstract_inverted_index.with | 65, 105 |
| abstract_inverted_index.array | 11 |
| abstract_inverted_index.class | 68 |
| abstract_inverted_index.first | 38 |
| abstract_inverted_index.infer | 55 |
| abstract_inverted_index.noisy | 96 |
| abstract_inverted_index.solve | 79 |
| abstract_inverted_index.these | 42 |
| abstract_inverted_index.types | 30 |
| abstract_inverted_index.where | 99 |
| abstract_inverted_index.agents | 6 |
| abstract_inverted_index.global | 14 |
| abstract_inverted_index.paper, | 47 |
| abstract_inverted_index.toward | 40 |
| abstract_inverted_index.between | 4 |
| abstract_inverted_index.cluster | 20 |
| abstract_inverted_index.complex | 1 |
| abstract_inverted_index.present | 49 |
| abstract_inverted_index.problem | 83 |
| abstract_inverted_index.propose | 74 |
| abstract_inverted_index.ranging | 16 |
| abstract_inverted_index.Decoding | 22 |
| abstract_inverted_index.approach | 91 |
| abstract_inverted_index.bilinear | 51, 82 |
| abstract_inverted_index.compared | 104 |
| abstract_inverted_index.coupling | 70 |
| abstract_inverted_index.existing | 106 |
| abstract_inverted_index.multiple | 5 |
| abstract_inverted_index.networks | 64 |
| abstract_inverted_index.validate | 89 |
| abstract_inverted_index.algorithm | 77 |
| abstract_inverted_index.datasets, | 98 |
| abstract_inverted_index.dynamics, | 15 |
| abstract_inverted_index.framework | 53 |
| abstract_inverted_index.functions | 61 |
| abstract_inverted_index.identical | 67 |
| abstract_inverted_index.intricate | 13 |
| abstract_inverted_index.iterative | 76 |
| abstract_inverted_index.networks, | 2 |
| abstract_inverted_index.resulting | 81 |
| abstract_inverted_index.simulated | 94 |
| abstract_inverted_index.structure | 25 |
| abstract_inverted_index.behaviors. | 44 |
| abstract_inverted_index.functions. | 71 |
| abstract_inverted_index.illustrate | 85 |
| abstract_inverted_index.intriguing | 43 |
| abstract_inverted_index.oscillator | 63 |
| abstract_inverted_index.approaches. | 107 |
| abstract_inverted_index.demonstrate | 101 |
| abstract_inverted_index.formations. | 21 |
| abstract_inverted_index.interaction | 60 |
| abstract_inverted_index.measurement | 34 |
| abstract_inverted_index.connectivity | 24, 58 |
| abstract_inverted_index.convergence. | 87 |
| abstract_inverted_index.experimental | 97 |
| abstract_inverted_index.interactions | 3, 32 |
| abstract_inverted_index.optimization | 52 |
| abstract_inverted_index.effectiveness | 103 |
| abstract_inverted_index.understanding | 41 |
| abstract_inverted_index.synchronization | 18 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |