Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN Article Swipe
Hansheng Xue
,
Luwei Yang
,
Wen Jiang
,
Wei Yi
,
Yi Hu
,
Yu Lin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.01024
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.01024
Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks. It has achieved great success on many tasks of network analysis such as link prediction and node classification. Most of existing network embedding algorithms focus on how to learn static homogeneous networks effectively. However, networks in the real world are more complex, e.g., networks may consist of several types of nodes and edges (called heterogeneous information) and may vary over time in terms of dynamic nodes and edges (called evolutionary patterns). Limited work has been done for network embedding of dynamic heterogeneous networks as it is challenging to learn both evolutionary and heterogeneous information simultaneously. In this paper, we propose a novel dynamic heterogeneous network embedding method, termed as DyHATR, which uses hierarchical attention to learn heterogeneous information and incorporates recurrent neural networks with temporal attention to capture evolutionary patterns. We benchmark our method on four real-world datasets for the task of link prediction. Experimental results show that DyHATR significantly outperforms several state-of-the-art baselines.
Related Topics
Concepts
Computer science
Embedding
Heterogeneous network
Benchmark (surveying)
Node (physics)
Recurrent neural network
Link (geometry)
Artificial intelligence
Dynamic network analysis
Task (project management)
Machine learning
Theoretical computer science
Artificial neural network
Computer network
Wireless network
Wireless
Structural engineering
Engineering
Management
Economics
Geography
Telecommunications
Geodesy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.01024
- https://arxiv.org/pdf/2004.01024
- OA Status
- green
- Cited By
- 10
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3014565706
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3014565706Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2004.01024Digital Object Identifier
- Title
-
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-01Full publication date if available
- Authors
-
Hansheng Xue, Luwei Yang, Wen Jiang, Wei Yi, Yi Hu, Yu LinList of authors in order
- Landing page
-
https://arxiv.org/abs/2004.01024Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2004.01024Direct 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/2004.01024Direct OA link when available
- Concepts
-
Computer science, Embedding, Heterogeneous network, Benchmark (surveying), Node (physics), Recurrent neural network, Link (geometry), Artificial intelligence, Dynamic network analysis, Task (project management), Machine learning, Theoretical computer science, Artificial neural network, Computer network, Wireless network, Wireless, Structural engineering, Engineering, Management, Economics, Geography, Telecommunications, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 3, 2022: 4, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3014565706 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2004.01024 |
| ids.doi | https://doi.org/10.48550/arxiv.2004.01024 |
| ids.mag | 3014565706 |
| ids.openalex | https://openalex.org/W3014565706 |
| fwci | |
| type | preprint |
| title | Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9995999932289124 |
| 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 | 0.998199999332428 |
| 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/T10887 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9448999762535095 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Bioinformatics and Genomic Networks |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8145765066146851 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C41608201 |
| concepts[1].level | 2 |
| concepts[1].score | 0.74570631980896 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q980509 |
| concepts[1].display_name | Embedding |
| concepts[2].id | https://openalex.org/C158207573 |
| concepts[2].level | 4 |
| concepts[2].score | 0.6758546829223633 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5747224 |
| concepts[2].display_name | Heterogeneous network |
| concepts[3].id | https://openalex.org/C185798385 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6479498147964478 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[3].display_name | Benchmark (surveying) |
| concepts[4].id | https://openalex.org/C62611344 |
| concepts[4].level | 2 |
| concepts[4].score | 0.577562153339386 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1062658 |
| concepts[4].display_name | Node (physics) |
| concepts[5].id | https://openalex.org/C147168706 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5518947243690491 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1457734 |
| concepts[5].display_name | Recurrent neural network |
| concepts[6].id | https://openalex.org/C2778753846 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5010778903961182 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6554239 |
| concepts[6].display_name | Link (geometry) |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.46598878502845764 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C13540734 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4608800411224365 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5318996 |
| concepts[8].display_name | Dynamic network analysis |
| concepts[9].id | https://openalex.org/C2780451532 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4480500817298889 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[9].display_name | Task (project management) |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.42434489727020264 |
| 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 | 0.35857439041137695 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[11].display_name | Theoretical computer science |
| concepts[12].id | https://openalex.org/C50644808 |
| concepts[12].level | 2 |
| concepts[12].score | 0.3518487215042114 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[12].display_name | Artificial neural network |
| concepts[13].id | https://openalex.org/C31258907 |
| concepts[13].level | 1 |
| concepts[13].score | 0.08360999822616577 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[13].display_name | Computer network |
| concepts[14].id | https://openalex.org/C108037233 |
| concepts[14].level | 3 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11375 |
| concepts[14].display_name | Wireless network |
| concepts[15].id | https://openalex.org/C555944384 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q249 |
| concepts[15].display_name | Wireless |
| concepts[16].id | https://openalex.org/C66938386 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[16].display_name | Structural engineering |
| concepts[17].id | https://openalex.org/C127413603 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[17].display_name | Engineering |
| concepts[18].id | https://openalex.org/C187736073 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[18].display_name | Management |
| concepts[19].id | https://openalex.org/C162324750 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[19].display_name | Economics |
| concepts[20].id | https://openalex.org/C205649164 |
| concepts[20].level | 0 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[20].display_name | Geography |
| concepts[21].id | https://openalex.org/C76155785 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[21].display_name | Telecommunications |
| concepts[22].id | https://openalex.org/C13280743 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[22].display_name | Geodesy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8145765066146851 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/embedding |
| keywords[1].score | 0.74570631980896 |
| keywords[1].display_name | Embedding |
| keywords[2].id | https://openalex.org/keywords/heterogeneous-network |
| keywords[2].score | 0.6758546829223633 |
| keywords[2].display_name | Heterogeneous network |
| keywords[3].id | https://openalex.org/keywords/benchmark |
| keywords[3].score | 0.6479498147964478 |
| keywords[3].display_name | Benchmark (surveying) |
| keywords[4].id | https://openalex.org/keywords/node |
| keywords[4].score | 0.577562153339386 |
| keywords[4].display_name | Node (physics) |
| keywords[5].id | https://openalex.org/keywords/recurrent-neural-network |
| keywords[5].score | 0.5518947243690491 |
| keywords[5].display_name | Recurrent neural network |
| keywords[6].id | https://openalex.org/keywords/link |
| keywords[6].score | 0.5010778903961182 |
| keywords[6].display_name | Link (geometry) |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.46598878502845764 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/dynamic-network-analysis |
| keywords[8].score | 0.4608800411224365 |
| keywords[8].display_name | Dynamic network analysis |
| keywords[9].id | https://openalex.org/keywords/task |
| keywords[9].score | 0.4480500817298889 |
| keywords[9].display_name | Task (project management) |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.42434489727020264 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[11].score | 0.35857439041137695 |
| keywords[11].display_name | Theoretical computer science |
| keywords[12].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[12].score | 0.3518487215042114 |
| keywords[12].display_name | Artificial neural network |
| keywords[13].id | https://openalex.org/keywords/computer-network |
| keywords[13].score | 0.08360999822616577 |
| keywords[13].display_name | Computer network |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2004.01024 |
| 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/2004.01024 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2004.01024 |
| locations[1].id | doi:10.48550/arxiv.2004.01024 |
| 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.2004.01024 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5102768105 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5263-4844 |
| authorships[0].author.display_name | Hansheng Xue |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I118347636 |
| authorships[0].affiliations[0].raw_affiliation_string | RESEARCH SCHOOL OF COMPUTER SCIENCE, THE AUSTRALIAN NATIONAL UNIVERSITY, CANBERRA, AUSTRALIA |
| authorships[0].institutions[0].id | https://openalex.org/I118347636 |
| authorships[0].institutions[0].ror | https://ror.org/019wvm592 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I118347636 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Australian National University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hansheng Xue |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | RESEARCH SCHOOL OF COMPUTER SCIENCE, THE AUSTRALIAN NATIONAL UNIVERSITY, CANBERRA, AUSTRALIA |
| authorships[1].author.id | https://openalex.org/A5061887584 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0358-0007 |
| authorships[1].author.display_name | Luwei Yang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I45928872 |
| authorships[1].affiliations[0].raw_affiliation_string | Alibaba Group, Hangzhou, China#TAB# |
| authorships[1].institutions[0].id | https://openalex.org/I45928872 |
| authorships[1].institutions[0].ror | https://ror.org/00k642b80 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I45928872 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Alibaba Group (China) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Luwei Yang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Alibaba Group, Hangzhou, China#TAB# |
| authorships[2].author.id | https://openalex.org/A5100715027 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8249-6879 |
| authorships[2].author.display_name | Wen Jiang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I45928872 |
| authorships[2].affiliations[0].raw_affiliation_string | Alibaba Group, Hangzhou, China#TAB# |
| authorships[2].institutions[0].id | https://openalex.org/I45928872 |
| authorships[2].institutions[0].ror | https://ror.org/00k642b80 |
| authorships[2].institutions[0].type | company |
| authorships[2].institutions[0].lineage | https://openalex.org/I45928872 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Alibaba Group (China) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Wen Jiang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Alibaba Group, Hangzhou, China#TAB# |
| authorships[3].author.id | https://openalex.org/A5100339151 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9878-7048 |
| authorships[3].author.display_name | Wei Yi |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I45928872 |
| authorships[3].affiliations[0].raw_affiliation_string | Alibaba Group, Hangzhou, China#TAB# |
| authorships[3].institutions[0].id | https://openalex.org/I45928872 |
| authorships[3].institutions[0].ror | https://ror.org/00k642b80 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I45928872 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Alibaba Group (China) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yi Wei |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Alibaba Group, Hangzhou, China#TAB# |
| authorships[4].author.id | https://openalex.org/A5101631294 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-9254-6331 |
| authorships[4].author.display_name | Yi Hu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I45928872 |
| authorships[4].affiliations[0].raw_affiliation_string | Alibaba Group, Hangzhou, China#TAB# |
| authorships[4].institutions[0].id | https://openalex.org/I45928872 |
| authorships[4].institutions[0].ror | https://ror.org/00k642b80 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I45928872 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Alibaba Group (China) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yi Hu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Alibaba Group, Hangzhou, China#TAB# |
| authorships[5].author.id | https://openalex.org/A5079714822 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-6339-2644 |
| authorships[5].author.display_name | Yu Lin |
| authorships[5].countries | AU |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I118347636 |
| authorships[5].affiliations[0].raw_affiliation_string | RESEARCH SCHOOL OF COMPUTER SCIENCE, THE AUSTRALIAN NATIONAL UNIVERSITY, CANBERRA, AUSTRALIA |
| authorships[5].institutions[0].id | https://openalex.org/I118347636 |
| authorships[5].institutions[0].ror | https://ror.org/019wvm592 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I118347636 |
| authorships[5].institutions[0].country_code | AU |
| authorships[5].institutions[0].display_name | Australian National University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Yu Lin |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | RESEARCH SCHOOL OF COMPUTER SCIENCE, THE AUSTRALIAN NATIONAL UNIVERSITY, CANBERRA, AUSTRALIA |
| 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/2004.01024 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9995999932289124 |
| 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/W4298287631, https://openalex.org/W2953061907, https://openalex.org/W1847088711, https://openalex.org/W4225394202, https://openalex.org/W3036642985, https://openalex.org/W3032952384, https://openalex.org/W2964335273, https://openalex.org/W2982145560, https://openalex.org/W2444507620, https://openalex.org/W4288391523 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2021 |
| counts_by_year[2].cited_by_count | 3 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2004.01024 |
| 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/2004.01024 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2004.01024 |
| primary_location.id | pmh:oai:arXiv.org:2004.01024 |
| 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/2004.01024 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2004.01024 |
| publication_date | 2020-04-01 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2998313947, https://openalex.org/W2986068433, https://openalex.org/W2985758772, https://openalex.org/W2974594200, https://openalex.org/W2907192581, https://openalex.org/W2951422523, https://openalex.org/W2932978649, https://openalex.org/W3104097132, https://openalex.org/W2743104969, https://openalex.org/W2064675550, https://openalex.org/W2953485314, https://openalex.org/W2950635152, https://openalex.org/W2963224980, https://openalex.org/W2906407596, https://openalex.org/W2962756421, https://openalex.org/W2969227524, https://openalex.org/W2945266622, https://openalex.org/W2963707260, https://openalex.org/W2787927827, https://openalex.org/W2963858333, https://openalex.org/W3105705953, https://openalex.org/W2798557793, https://openalex.org/W2755088640, https://openalex.org/W2806983170, https://openalex.org/W2808087697, https://openalex.org/W2964811671, https://openalex.org/W2963885834, https://openalex.org/W2922924332, https://openalex.org/W2954691982, https://openalex.org/W2561827022, https://openalex.org/W2962975498, https://openalex.org/W2740934577, https://openalex.org/W3122931143, https://openalex.org/W2624431344, https://openalex.org/W2808361044, https://openalex.org/W2937762029, https://openalex.org/W2962767366, https://openalex.org/W3015285715, https://openalex.org/W3014623641, https://openalex.org/W2804558096, https://openalex.org/W2964015378, https://openalex.org/W2984765336 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 115 |
| abstract_inverted_index.In | 110 |
| abstract_inverted_index.It | 15 |
| abstract_inverted_index.We | 145 |
| abstract_inverted_index.as | 27, 98, 123 |
| abstract_inverted_index.in | 50, 76 |
| abstract_inverted_index.is | 100 |
| abstract_inverted_index.it | 99 |
| abstract_inverted_index.of | 7, 13, 23, 34, 61, 64, 78, 94, 156 |
| abstract_inverted_index.on | 20, 40, 149 |
| abstract_inverted_index.to | 3, 42, 102, 129, 141 |
| abstract_inverted_index.we | 113 |
| abstract_inverted_index.and | 30, 66, 71, 81, 106, 133 |
| abstract_inverted_index.are | 54 |
| abstract_inverted_index.for | 91, 153 |
| abstract_inverted_index.has | 16, 88 |
| abstract_inverted_index.how | 41 |
| abstract_inverted_index.may | 59, 72 |
| abstract_inverted_index.our | 147 |
| abstract_inverted_index.the | 51, 154 |
| abstract_inverted_index.Most | 33 |
| abstract_inverted_index.aims | 2 |
| abstract_inverted_index.been | 89 |
| abstract_inverted_index.both | 104 |
| abstract_inverted_index.done | 90 |
| abstract_inverted_index.four | 150 |
| abstract_inverted_index.link | 28, 157 |
| abstract_inverted_index.many | 21 |
| abstract_inverted_index.more | 55 |
| abstract_inverted_index.node | 31 |
| abstract_inverted_index.over | 74 |
| abstract_inverted_index.real | 52 |
| abstract_inverted_index.show | 161 |
| abstract_inverted_index.such | 26 |
| abstract_inverted_index.task | 155 |
| abstract_inverted_index.that | 162 |
| abstract_inverted_index.this | 111 |
| abstract_inverted_index.time | 75 |
| abstract_inverted_index.uses | 126 |
| abstract_inverted_index.vary | 73 |
| abstract_inverted_index.with | 138 |
| abstract_inverted_index.work | 87 |
| abstract_inverted_index.e.g., | 57 |
| abstract_inverted_index.edges | 67, 82 |
| abstract_inverted_index.focus | 39 |
| abstract_inverted_index.great | 18 |
| abstract_inverted_index.learn | 4, 43, 103, 130 |
| abstract_inverted_index.nodes | 8, 65, 80 |
| abstract_inverted_index.novel | 116 |
| abstract_inverted_index.tasks | 22 |
| abstract_inverted_index.terms | 77 |
| abstract_inverted_index.types | 63 |
| abstract_inverted_index.which | 125 |
| abstract_inverted_index.while | 9 |
| abstract_inverted_index.world | 53 |
| abstract_inverted_index.DyHATR | 163 |
| abstract_inverted_index.method | 148 |
| abstract_inverted_index.neural | 136 |
| abstract_inverted_index.paper, | 112 |
| abstract_inverted_index.static | 44 |
| abstract_inverted_index.termed | 122 |
| abstract_inverted_index.(called | 68, 83 |
| abstract_inverted_index.DyHATR, | 124 |
| abstract_inverted_index.Limited | 86 |
| abstract_inverted_index.Network | 0 |
| abstract_inverted_index.capture | 142 |
| abstract_inverted_index.consist | 60 |
| abstract_inverted_index.dynamic | 79, 95, 117 |
| abstract_inverted_index.method, | 121 |
| abstract_inverted_index.network | 24, 36, 92, 119 |
| abstract_inverted_index.propose | 114 |
| abstract_inverted_index.results | 160 |
| abstract_inverted_index.several | 62, 166 |
| abstract_inverted_index.success | 19 |
| abstract_inverted_index.However, | 48 |
| abstract_inverted_index.achieved | 17 |
| abstract_inverted_index.analysis | 25 |
| abstract_inverted_index.complex, | 56 |
| abstract_inverted_index.datasets | 152 |
| abstract_inverted_index.existing | 35 |
| abstract_inverted_index.networks | 46, 49, 58, 97, 137 |
| abstract_inverted_index.temporal | 139 |
| abstract_inverted_index.attention | 128, 140 |
| abstract_inverted_index.benchmark | 146 |
| abstract_inverted_index.capturing | 10 |
| abstract_inverted_index.embedding | 1, 37, 93, 120 |
| abstract_inverted_index.networks. | 14 |
| abstract_inverted_index.patterns. | 144 |
| abstract_inverted_index.recurrent | 135 |
| abstract_inverted_index.structure | 11 |
| abstract_inverted_index.algorithms | 38 |
| abstract_inverted_index.baselines. | 168 |
| abstract_inverted_index.patterns). | 85 |
| abstract_inverted_index.prediction | 29 |
| abstract_inverted_index.real-world | 151 |
| abstract_inverted_index.challenging | 101 |
| abstract_inverted_index.homogeneous | 45 |
| abstract_inverted_index.information | 12, 108, 132 |
| abstract_inverted_index.outperforms | 165 |
| abstract_inverted_index.prediction. | 158 |
| abstract_inverted_index.Experimental | 159 |
| abstract_inverted_index.effectively. | 47 |
| abstract_inverted_index.evolutionary | 84, 105, 143 |
| abstract_inverted_index.hierarchical | 127 |
| abstract_inverted_index.incorporates | 134 |
| abstract_inverted_index.information) | 70 |
| abstract_inverted_index.heterogeneous | 69, 96, 107, 118, 131 |
| abstract_inverted_index.significantly | 164 |
| abstract_inverted_index.classification. | 32 |
| abstract_inverted_index.low-dimensional | 5 |
| abstract_inverted_index.representations | 6 |
| abstract_inverted_index.simultaneously. | 109 |
| abstract_inverted_index.state-of-the-art | 167 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |