# Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph

arXiv: https://arxiv.org/abs/2210.00450 · HTML: https://mlsanity.com/paper/2210.00450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2005.07427/) (2020)
- [Predicting citation counts based on deep neural network learning techniques](https://mlsanity.com/paper/1809.04365/) (2018)
- [Structured Citation Trend Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2104.02562/) (2021)
- [Predicting the Citations of Scholarly Paper](https://mlsanity.com/paper/2008.05013/) (2020)
- [Longitudinal Citation Prediction using Temporal Graph Neural Networks](https://mlsanity.com/paper/2012.05742/) (2020)
- [CasGCN: Predicting future cascade growth based on information diffusion graph](https://mlsanity.com/paper/2009.05152/) (2020)
- [Temporal Network Embedding via Tensor Factorization](https://mlsanity.com/paper/2108.09837/) (2021)
- [Estimation of COVID-19 spread curves integrating global data and borrowing information](https://mlsanity.com/paper/2005.00662/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)

## Abstract (arXiv)

Predicting the impact of publications in science and technology has become an important research area, which is useful in various real world scenarios such as technology investment, research direction selection, and technology policymaking. Citation trajectory prediction is one of the most popular tasks in this area. Existing approaches mainly rely on mining temporal and graph data from academic articles. Some recent methods are capable of handling cold-start prediction by aggregating metadata features of new publications. However, the implicit factors causing citations and the richer information from handling temporal and attribute features still need to be explored. In this paper, we propose CTPIR, a new citation trajectory prediction framework that is able to represent the influence (the momentum of citation) of either new or existing publications using the history information of all their attributes. Our framework is composed of three modules: difference-preserved graph embedding, fine-grained influence representation, and learning-based trajectory calculation. To test the effectiveness of our framework in more situations, we collect and construct a new temporal knowledge graph dataset from the real world, named AIPatent, which stems from global patents in the field of artificial intelligence. Experiments are conducted on both the APS academic dataset and our contributed AIPatent dataset. The results demonstrate the strengths of our approach in the citation trajectory prediction task.