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H2CGL: Modeling dynamics of citation network for impact prediction · Study

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ArticleH2CGL: Modeling dynamics of citation network for impact prediction

Guoxiu He2023

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Articlegreen2023

H2CGL: Modeling dynamics of citation network for impact prediction

Guoxiu He · Zhikai Xue · Zhuoren Jiang · Yangyang Kang · Star X. Zhao · Wei Ping Lu

19 citations·0/0 files stored·Information Processing & Management

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scientometrics and bibliometrics research Recommender Systems and Techniques Advanced Graph Neural Networks

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References

2008ArnetMiner 2023Papers and patents are becoming less disruptive over time 2013Quantifying Long-Term Scientific Impact 2016Quantifying the evolution of individual scientific impact 2004Sleeping Beauties in science 2016A Dynamic Network Measure of Technological Change 2020S2ORC: The Semantic Scholar Open Research Corpus 2021Slowed canonical progress in large fields of science

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2025Forecasting high-impact research topics via machine learning on evolving knowledge graphs

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.

2022A review of scientific impact prediction: tasks, features and methodsarticle · 10 shared references 2024Predicting citation impact of academic papers across research areas using multiple models and early citationsarticle · 9 shared references 2021Early indicators of scientific impact: Predicting citations with altmetricsarticle · 7 shared references 2021Fine-grained citation count prediction via a transformer-based model with among-attention mechanismarticle · 7 shared references 2021Utilizing citation network structure to predict paper citation counts: A Deep learning approacharticle · 6 shared references 2023Identifying potentially excellent publications using a citation-based machine learning approacharticle · 6 shared references

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Year2023

Typearticle

Published2023-09-28

VenueInformation Processing & Management

ISSN-L0020-0271

Languageen

Citations19

Files0 public / 0

Shelfscientometrics and bibliometrics research

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doi10.1016/j.ipm.2023.103512

openalex_workW4387150591

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