Rumor detection driven by graph attention capsule network on dynamic propagation structures Article Swipe

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Peng Yang , Juncheng Leng , Guangzhen Zhao , Wenjun Li , Haisheng Fang ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1007/s11227-022-04831-7

Related Topics

Rumor

Computer Science

Artificial Intelligence

Data Mining

Theoretical Computer Science

Machine Learning

Engineering

Structural Engineering

Public Relations

Biochemistry

Chemistry

Concepts

Rumor Computer science Substructure Graph Artificial neural network Artificial intelligence ENCODE Data mining Theoretical computer science Machine learning Engineering Structural engineering Public relations Gene Biochemistry Political science Chemistry

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Type: article
Language: en
Landing Page: https://doi.org/10.1007/s11227-022-04831-7
OA Status: green
Cited By: 10
References: 27
Related Works: 10
OpenAlex ID: https://openalex.org/W4304689663

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OpenAlex ID: https://openalex.org/W4304689663

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.1007/s11227-022-04831-7

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Title: Rumor detection driven by graph attention capsule network on dynamic propagation structures

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-10-12

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Authors: Peng Yang, Juncheng Leng, Guangzhen Zhao, Wenjun Li, Haisheng Fang

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Landing page: https://doi.org/10.1007/s11227-022-04831-7

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Open access: Yes

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OA status: green

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OA URL: https://www.ncbi.nlm.nih.gov/pmc/articles/9554865

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Concepts: Rumor, Computer science, Substructure, Graph, Artificial neural network, Artificial intelligence, ENCODE, Data mining, Theoretical computer science, Machine learning, Engineering, Structural engineering, Public relations, Gene, Biochemistry, Political science, Chemistry

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Cited by: 10

Total citation count in OpenAlex
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References (count): 27

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Related works (count): 10

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countries_distinct_count	1
institutions_distinct_count	5
corresponding_institution_ids	https://openalex.org/I76569877
citation_normalized_percentile.value	0.93680296
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True