TimeGraphs: Graph-based Temporal Reasoning Article Swipe
Paridhi Maheshwari
,
Hong‐Yu Ren
,
Yanan Wang
,
Rok Sosič
,
Jure Leskovec
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.03134
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.03134
Many real-world systems exhibit temporal, dynamic behaviors, which are captured as time series of complex agent interactions. To perform temporal reasoning, current methods primarily encode temporal dynamics through simple sequence-based models. However, in general these models fail to efficiently capture the full spectrum of rich dynamics in the input, since the dynamics is not uniformly distributed. In particular, relevant information might be harder to extract and computing power is wasted for processing all individual timesteps, even if they contain no significant changes or no new information. Here we propose TimeGraphs, a novel approach that characterizes dynamic interactions as a hierarchical temporal graph, diverging from traditional sequential representations. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. Adopting a self-supervised method, TimeGraphs constructs a multi-level event hierarchy from a temporal input, which is then used to efficiently reason about the unevenly distributed dynamics. This construction process is scalable and incremental to accommodate streaming data. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset. The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks. Our approach obtains state-of-the-art performance and leads to a performance increase of up to 12.2% on event prediction and recognition tasks over current approaches. Our experiments further demonstrate a wide array of capabilities including zero-shot generalization, robustness in case of data sparsity, and adaptability to streaming data flow.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.03134
- https://arxiv.org/pdf/2401.03134
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390722792
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390722792Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2401.03134Digital Object Identifier
- Title
-
TimeGraphs: Graph-based Temporal ReasoningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-06Full publication date if available
- Authors
-
Paridhi Maheshwari, Hong‐Yu Ren, Yanan Wang, Rok Sosič, Jure LeskovecList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.03134Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.03134Direct 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/2401.03134Direct OA link when available
- Concepts
-
Computer science, Scalability, Robustness (evolution), ENCODE, Adaptability, Graph, Artificial intelligence, Theoretical computer science, Machine learning, Database, Ecology, Gene, Biology, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4390722792 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2401.03134 |
| ids.doi | https://doi.org/10.48550/arxiv.2401.03134 |
| ids.openalex | https://openalex.org/W4390722792 |
| fwci | |
| type | preprint |
| title | TimeGraphs: Graph-based Temporal Reasoning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12205 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.996399998664856 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1711 |
| topics[0].subfield.display_name | Signal Processing |
| topics[0].display_name | Time Series Analysis and Forecasting |
| topics[1].id | https://openalex.org/T12761 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9882000088691711 |
| 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 | Data Stream Mining Techniques |
| topics[2].id | https://openalex.org/T11303 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9728000164031982 |
| 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 | Bayesian Modeling and Causal Inference |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7953577637672424 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C48044578 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6455475091934204 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q727490 |
| concepts[1].display_name | Scalability |
| concepts[2].id | https://openalex.org/C63479239 |
| concepts[2].level | 3 |
| concepts[2].score | 0.571833610534668 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[2].display_name | Robustness (evolution) |
| concepts[3].id | https://openalex.org/C66746571 |
| concepts[3].level | 3 |
| concepts[3].score | 0.509917140007019 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1134833 |
| concepts[3].display_name | ENCODE |
| concepts[4].id | https://openalex.org/C177606310 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5014324188232422 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5674297 |
| concepts[4].display_name | Adaptability |
| concepts[5].id | https://openalex.org/C132525143 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4863224923610687 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[5].display_name | Graph |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.46385738253593445 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C80444323 |
| concepts[7].level | 1 |
| concepts[7].score | 0.41436487436294556 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[7].display_name | Theoretical computer science |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.36596283316612244 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C77088390 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[9].display_name | Database |
| concepts[10].id | https://openalex.org/C18903297 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[10].display_name | Ecology |
| concepts[11].id | https://openalex.org/C104317684 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[11].display_name | Gene |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| concepts[13].id | https://openalex.org/C185592680 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[13].display_name | Chemistry |
| concepts[14].id | https://openalex.org/C55493867 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[14].display_name | Biochemistry |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7953577637672424 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/scalability |
| keywords[1].score | 0.6455475091934204 |
| keywords[1].display_name | Scalability |
| keywords[2].id | https://openalex.org/keywords/robustness |
| keywords[2].score | 0.571833610534668 |
| keywords[2].display_name | Robustness (evolution) |
| keywords[3].id | https://openalex.org/keywords/encode |
| keywords[3].score | 0.509917140007019 |
| keywords[3].display_name | ENCODE |
| keywords[4].id | https://openalex.org/keywords/adaptability |
| keywords[4].score | 0.5014324188232422 |
| keywords[4].display_name | Adaptability |
| keywords[5].id | https://openalex.org/keywords/graph |
| keywords[5].score | 0.4863224923610687 |
| keywords[5].display_name | Graph |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.46385738253593445 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[7].score | 0.41436487436294556 |
| keywords[7].display_name | Theoretical computer science |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.36596283316612244 |
| keywords[8].display_name | Machine learning |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2401.03134 |
| 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/2401.03134 |
| 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/2401.03134 |
| locations[1].id | doi:10.48550/arxiv.2401.03134 |
| 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 | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| 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.2401.03134 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5051903902 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6831-9091 |
| authorships[0].author.display_name | Paridhi Maheshwari |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Maheshwari, Paridhi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101912684 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4733-7607 |
| authorships[1].author.display_name | Hong‐Yu Ren |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ren, Hongyu |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5115694972 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7179-4686 |
| authorships[2].author.display_name | Yanan Wang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Wang, Yanan |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5087005512 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0723-9172 |
| authorships[3].author.display_name | Rok Sosič |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Sosic, Rok |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5091272738 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5411-923X |
| authorships[4].author.display_name | Jure Leskovec |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Leskovec, Jure |
| 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/2401.03134 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | TimeGraphs: Graph-based Temporal Reasoning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12205 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.996399998664856 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1711 |
| primary_topic.subfield.display_name | Signal Processing |
| primary_topic.display_name | Time Series Analysis and Forecasting |
| related_works | https://openalex.org/W2357124094, https://openalex.org/W2387399993, https://openalex.org/W2389739210, https://openalex.org/W2348924972, https://openalex.org/W2365736347, https://openalex.org/W2047454415, https://openalex.org/W2070040999, https://openalex.org/W2387293848, https://openalex.org/W2250140200, https://openalex.org/W3121791438 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2401.03134 |
| 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/2401.03134 |
| 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/2401.03134 |
| primary_location.id | pmh:oai:arXiv.org:2401.03134 |
| 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/2401.03134 |
| 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/2401.03134 |
| publication_date | 2024-01-06 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 90, 98, 113, 125, 130, 135, 173, 195, 209, 229 |
| abstract_inverted_index.In | 56 |
| abstract_inverted_index.To | 17 |
| abstract_inverted_index.We | 161 |
| abstract_inverted_index.as | 10, 97 |
| abstract_inverted_index.be | 61 |
| abstract_inverted_index.if | 76 |
| abstract_inverted_index.in | 32, 46, 238 |
| abstract_inverted_index.is | 52, 68, 139, 153 |
| abstract_inverted_index.no | 79, 83 |
| abstract_inverted_index.of | 13, 43, 192, 197, 212, 232, 240 |
| abstract_inverted_index.on | 164, 194, 216 |
| abstract_inverted_index.or | 82 |
| abstract_inverted_index.to | 37, 63, 142, 157, 208, 214, 245 |
| abstract_inverted_index.up | 213 |
| abstract_inverted_index.we | 87 |
| abstract_inverted_index.Our | 107, 201, 225 |
| abstract_inverted_index.The | 185 |
| abstract_inverted_index.all | 72 |
| abstract_inverted_index.and | 65, 155, 179, 190, 206, 219, 243 |
| abstract_inverted_index.are | 8 |
| abstract_inverted_index.for | 70 |
| abstract_inverted_index.new | 84 |
| abstract_inverted_index.not | 53 |
| abstract_inverted_index.the | 40, 47, 50, 110, 146, 176, 180 |
| abstract_inverted_index.Here | 86 |
| abstract_inverted_index.MOMA | 181 |
| abstract_inverted_index.Many | 0 |
| abstract_inverted_index.This | 150 |
| abstract_inverted_index.both | 188 |
| abstract_inverted_index.case | 239 |
| abstract_inverted_index.data | 241, 247 |
| abstract_inverted_index.even | 75 |
| abstract_inverted_index.fail | 36 |
| abstract_inverted_index.from | 103, 134 |
| abstract_inverted_index.full | 41 |
| abstract_inverted_index.over | 222 |
| abstract_inverted_index.rich | 44 |
| abstract_inverted_index.that | 93 |
| abstract_inverted_index.then | 140 |
| abstract_inverted_index.they | 77 |
| abstract_inverted_index.time | 11, 122 |
| abstract_inverted_index.used | 141 |
| abstract_inverted_index.wide | 230 |
| abstract_inverted_index.with | 167 |
| abstract_inverted_index.12.2% | 215 |
| abstract_inverted_index.about | 145 |
| abstract_inverted_index.agent | 15, 170 |
| abstract_inverted_index.array | 231 |
| abstract_inverted_index.data. | 160 |
| abstract_inverted_index.event | 132, 217 |
| abstract_inverted_index.flow. | 248 |
| abstract_inverted_index.game, | 178 |
| abstract_inverted_index.human | 182 |
| abstract_inverted_index.leads | 207 |
| abstract_inverted_index.might | 60 |
| abstract_inverted_index.novel | 91 |
| abstract_inverted_index.power | 67 |
| abstract_inverted_index.range | 196 |
| abstract_inverted_index.since | 49 |
| abstract_inverted_index.tasks | 221 |
| abstract_inverted_index.these | 34 |
| abstract_inverted_index.using | 112 |
| abstract_inverted_index.which | 7, 138 |
| abstract_inverted_index.across | 120 |
| abstract_inverted_index.encode | 24 |
| abstract_inverted_index.graph, | 101 |
| abstract_inverted_index.harder | 62 |
| abstract_inverted_index.input, | 48, 137 |
| abstract_inverted_index.models | 35, 109 |
| abstract_inverted_index.reason | 144 |
| abstract_inverted_index.series | 12 |
| abstract_inverted_index.simple | 28 |
| abstract_inverted_index.tasks. | 200 |
| abstract_inverted_index.wasted | 69 |
| abstract_inverted_index.capture | 39 |
| abstract_inverted_index.changes | 81 |
| abstract_inverted_index.compact | 114 |
| abstract_inverted_index.complex | 14 |
| abstract_inverted_index.contain | 78 |
| abstract_inverted_index.current | 21, 223 |
| abstract_inverted_index.diverse | 121 |
| abstract_inverted_index.dynamic | 5, 95, 169 |
| abstract_inverted_index.exhibit | 3 |
| abstract_inverted_index.extract | 64 |
| abstract_inverted_index.further | 227 |
| abstract_inverted_index.general | 33 |
| abstract_inverted_index.method, | 127 |
| abstract_inverted_index.methods | 22 |
| abstract_inverted_index.models. | 30 |
| abstract_inverted_index.obtains | 203 |
| abstract_inverted_index.perform | 18 |
| abstract_inverted_index.process | 152 |
| abstract_inverted_index.propose | 88 |
| abstract_inverted_index.results | 186 |
| abstract_inverted_index.scales. | 123 |
| abstract_inverted_index.systems | 2 |
| abstract_inverted_index.through | 27 |
| abstract_inverted_index.Adopting | 124 |
| abstract_inverted_index.However, | 31 |
| abstract_inverted_index.activity | 183 |
| abstract_inverted_index.adaptive | 118 |
| abstract_inverted_index.approach | 92, 108, 202 |
| abstract_inverted_index.captured | 9 |
| abstract_inverted_index.complex, | 168 |
| abstract_inverted_index.dataset. | 184 |
| abstract_inverted_index.datasets | 166 |
| abstract_inverted_index.dynamics | 26, 45, 51 |
| abstract_inverted_index.enabling | 117 |
| abstract_inverted_index.evaluate | 162 |
| abstract_inverted_index.football | 174 |
| abstract_inverted_index.increase | 211 |
| abstract_inverted_index.multiple | 165 |
| abstract_inverted_index.relevant | 58 |
| abstract_inverted_index.scalable | 154 |
| abstract_inverted_index.spectrum | 42 |
| abstract_inverted_index.temporal | 19, 25, 100, 136, 198 |
| abstract_inverted_index.unevenly | 147 |
| abstract_inverted_index.computing | 66 |
| abstract_inverted_index.diverging | 102 |
| abstract_inverted_index.dynamics. | 149 |
| abstract_inverted_index.hierarchy | 133 |
| abstract_inverted_index.including | 172, 234 |
| abstract_inverted_index.primarily | 23 |
| abstract_inverted_index.reasoning | 119, 199 |
| abstract_inverted_index.sparsity, | 242 |
| abstract_inverted_index.streaming | 159, 246 |
| abstract_inverted_index.temporal, | 4 |
| abstract_inverted_index.uniformly | 54 |
| abstract_inverted_index.zero-shot | 235 |
| abstract_inverted_index.Resistance | 177 |
| abstract_inverted_index.TimeGraphs | 128, 163, 193 |
| abstract_inverted_index.behaviors, | 6 |
| abstract_inverted_index.constructs | 129 |
| abstract_inverted_index.efficiency | 191 |
| abstract_inverted_index.individual | 73 |
| abstract_inverted_index.prediction | 218 |
| abstract_inverted_index.processing | 71 |
| abstract_inverted_index.real-world | 1 |
| abstract_inverted_index.reasoning, | 20 |
| abstract_inverted_index.robustness | 189, 237 |
| abstract_inverted_index.sequential | 105 |
| abstract_inverted_index.simulator, | 175 |
| abstract_inverted_index.timesteps, | 74 |
| abstract_inverted_index.TimeGraphs, | 89 |
| abstract_inverted_index.accommodate | 158 |
| abstract_inverted_index.approaches. | 224 |
| abstract_inverted_index.demonstrate | 187, 228 |
| abstract_inverted_index.distributed | 148 |
| abstract_inverted_index.efficiently | 38, 143 |
| abstract_inverted_index.experiments | 226 |
| abstract_inverted_index.graph-based | 115 |
| abstract_inverted_index.incremental | 156 |
| abstract_inverted_index.information | 59 |
| abstract_inverted_index.multi-level | 131 |
| abstract_inverted_index.particular, | 57 |
| abstract_inverted_index.performance | 205, 210 |
| abstract_inverted_index.recognition | 220 |
| abstract_inverted_index.significant | 80 |
| abstract_inverted_index.traditional | 104 |
| abstract_inverted_index.adaptability | 244 |
| abstract_inverted_index.capabilities | 233 |
| abstract_inverted_index.construction | 151 |
| abstract_inverted_index.distributed. | 55 |
| abstract_inverted_index.hierarchical | 99 |
| abstract_inverted_index.information. | 85 |
| abstract_inverted_index.interactions | 96, 111 |
| abstract_inverted_index.characterizes | 94 |
| abstract_inverted_index.interactions, | 171 |
| abstract_inverted_index.interactions. | 16 |
| abstract_inverted_index.sequence-based | 29 |
| abstract_inverted_index.generalization, | 236 |
| abstract_inverted_index.representation, | 116 |
| abstract_inverted_index.self-supervised | 126 |
| abstract_inverted_index.representations. | 106 |
| abstract_inverted_index.state-of-the-art | 204 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |