Citation Prediction
8 papers with code • 5 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Citation Prediction
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REFCAT
Latest papers with no code
When Large Language Models Meet Citation: A Survey
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
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
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
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
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
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
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
Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time.
