Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model

Główne pojęcia

The author proposes the Dynamic Impact Single-Event Embedding Model (DISEE) to reconcile impact quantification with graph representation learning in single-event networks.

Streszczenie

The content discusses the importance of understanding scientific research dynamics through citation networks. It introduces the DISEE model, which combines impact characterization with latent distance modeling for accurate link prediction and impact assessment in citation networks.
Key points:

Importance of understanding scientific research dynamics.
Introduction of the DISEE model for reconciling impact quantification and graph representation learning.
Discussion on single-event networks and their analysis through citation networks.
Detailed explanation of the DISEE model's components and its application in link prediction tasks.
Comparison with traditional models and baselines to showcase the effectiveness of DISEE.
Visualization of impact functions and embedding spaces for target papers over time.

Statystyki

Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024, Valencia, Spain.
Total number of links: 160,039 in Artificial network.
Machine Learning network: 22,540 target nodes, 148,703 source nodes, 526,226 total links.

Cytaty

"Understanding the structure and dynamics of scientific research has become an important area of research." - Content
"Citation networks are dynamic structures that require advanced modeling approaches." - Content

Kluczowe wnioski z

Time to Cite

by Niko... o arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00032.pdf

Głębsze pytania

How can the DISEE model be extended to predict future citation patterns accurately

To extend the DISEE model for accurate prediction of future citation patterns, we can incorporate additional features and techniques. One approach is to integrate node features such as author reputation, journal impact factor, or keyword relevance into the model. By considering these factors along with temporal dynamics captured by the impact function, we can enhance the predictive power of DISEE. Additionally, leveraging advanced machine learning algorithms like Graph Neural Networks (GNNs) can help in capturing complex relationships and patterns within citation networks for more precise predictions.

What are the implications of reconciling impact quantification with graph representation learning in other fields beyond scientific research

The implications of reconciling impact quantification with graph representation learning go beyond scientific research and can be applied in various fields. In social network analysis, this approach could help understand how information spreads through networks and identify influential nodes or communities. In finance, it could aid in predicting market trends based on historical trading data and investor interactions. Furthermore, in healthcare analytics, combining impact quantification with graph representation learning could improve patient outcome predictions by analyzing medical records and treatment pathways.

How can traditional models benefit from incorporating dynamic impact assessment techniques like those proposed by DISEE

Traditional models stand to benefit significantly from incorporating dynamic impact assessment techniques like those proposed by DISEE. By integrating dynamic modeling approaches into traditional methods such as linear regression or logistic regression used for impact prediction tasks, these models can capture the evolving nature of interactions between entities over time more accurately. This integration allows traditional models to account for changing trends and preferences that influence outcomes in various domains such as marketing strategies based on customer behavior or academic paper citations influenced by emerging research trends.