Authors: Razieh Shirzadkhani, Shenyang Huang, Elahe Kooshafar, Reihaneh Rabbany, Farimah Poursafaei

Published on: February 06, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.03651

Summary

• What is new: Introduction of TGX, a Python package specifically designed for analyzing temporal (evolving) networks, which contrasts with existing libraries focused mostly on static graphs.
• Why this is important: The dynamic aspect of real-world networks is not effectively addressed by current software libraries, which are mainly tailored for static graph analysis.
• What the research proposes: TGX offers a comprehensive way to load, process, and analyze temporal graphs, including functionalities for data discretization, node subsampling, and various analysis measures to study the evolving nature of graphs.
• Results: TGX provides access to built-in and external datasets for analysis, facilitates faster data processing through discretization and subsampling, and offers visualization tools for better understanding of temporal patterns.

Technical Details

Technological frameworks used: Python

Models used: nan

Data used: Eleven built-in datasets and eight Temporal Graph Benchmark (TGB) datasets, plus support for .csv formats.

Potential Impact

Social network platforms, academic citation databases, and businesses tracking user interactions could benefit from TGX by gaining better insights into the dynamic nature of their networks.

Want to implement this idea in a business?

We have generated a startup concept here: TimeFlow Analytics.