Temporal Graph Theoretic Analysis of Geopolitical Dynamics in the U.S. Entity List
October 24, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yunsen Lei, Kexin Bai, Quan Li, H. Howie Huang
arXiv ID
2510.21962
Category
cs.IR: Information Retrieval
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Export controls have become one of America's most prominent tools of economic statecraft. They aim to block rival countries' access to sensitive technologies, safeguard U.S. supply chains, protect national security, and shape geopolitical competition. Among various instruments, the U.S. Entity List has emerged as the most salient, yet its dynamics remain underexplored. This paper introduces a novel temporal graph framework that transforms the Entity List documents from a static registry of foreign entities of concern into a dynamic representation of geopolitical strategy. We construct the first event-based dataset of U.S. government foreign entity designations and model them as a temporal bipartite graph. Building on this representation, we develop a multi-level analytical approach that reveals shifting roles, enforcement strategy, and broader sanction ecosystems. Applied to 25 years of data, the framework uncovers dynamic patterns of escalation, persistence, and coordination that static views cannot capture. More broadly, our study demonstrates how temporal graph analysis offers systematic computational insights into the geopolitical dynamics of export controls.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted