Citation Prediction

8 papers with code • 5 benchmarks • 2 datasets

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Latest papers with no code

When Large Language Models Meet Citation: A Survey

no code yet • 18 Sep 2023

Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs.

Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

no code yet • 27 May 2023

To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.

Prototype-Based Interpretability for Legal Citation Prediction

no code yet • 25 May 2023

Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact.

Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding

no code yet • 23 May 2023

Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery.

Semantic Analysis for Automated Evaluation of the Potential Impact of Research Articles

no code yet • 26 Apr 2021

We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.

Structured Citation Trend Prediction Using Graph Neural Networks

no code yet • 6 Apr 2021

Academic citation graphs represent citation relationships between publications across the full range of academic fields.

SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction

no code yet • 21 Dec 2020

We also show the merit of using more training data and longer input for number of citations prediction.

Longitudinal Citation Prediction using Temporal Graph Neural Networks

no code yet • 10 Dec 2020

Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time.